paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
56af2c3a-8c68-4f74-8156-605a759a6547 | graph-denoising-diffusion-for-inverse-protein | 2306.16819 | null | https://arxiv.org/abs/2306.16819v1 | https://arxiv.org/pdf/2306.16819v1.pdf | Graph Denoising Diffusion for Inverse Protein Folding | Inverse protein folding is challenging due to its inherent one-to-many mapping characteristic, where numerous possible amino acid sequences can fold into a single, identical protein backbone. This task involves not only identifying viable sequences but also representing the sheer diversity of potential solutions. Howev... | ['Yu Guang Wang', 'Pietro Liò', 'Yiqing Shen', 'Bingxin Zhou', 'Kai Yi'] | 2023-06-29 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 5.84289074e-01 5.55194952e-02 8.96727666e-03 -2.42435977e-01
-7.77362585e-01 -7.34602451e-01 4.33214784e-01 -5.57385758e-02
-6.29074126e-02 1.17218554e+00 4.58693624e-01 -2.63543814e-01
6.38098791e-02 -8.36741805e-01 -1.04529357e+00 -1.26068282e+00
2.24006012e-01 9.92424488e-01 -3.06253694e-02 -2.76716143... | [4.748229503631592, 5.5831217765808105] |
f0e6e135-6b29-4231-9e74-ceaac9157a34 | direct-learning-of-sparse-changes-in-markov | 1304.6803 | null | http://arxiv.org/abs/1304.6803v5 | http://arxiv.org/pdf/1304.6803v5.pdf | Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation | We propose a new method for detecting changes in Markov network structure
between two sets of samples. Instead of naively fitting two Markov network
models separately to the two data sets and figuring out their difference, we
\emph{directly} learn the network structure change by estimating the ratio of
Markov network m... | ['John A. Quinn', 'Taiji Suzuki', 'Michael U. Gutmann', 'Masashi Sugiyama', 'Song Liu'] | 2013-04-25 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 3.08418602e-01 3.18081170e-01 -4.95724410e-01 -3.26082885e-01
-5.55962861e-01 -8.57867360e-01 3.99607569e-01 2.69456189e-02
-1.73967302e-01 4.66166049e-01 1.30628660e-01 -4.85089928e-01
-3.88806999e-01 -6.58668876e-01 -6.58520401e-01 -6.44914210e-01
-8.35057124e-02 3.41097832e-01 7.09369704e-02 4.63339299... | [6.961027145385742, 5.446763038635254] |
96c3f88d-5177-40aa-85b2-7474f6db6a28 | counting-crowds-in-bad-weather | 2306.01209 | null | https://arxiv.org/abs/2306.01209v1 | https://arxiv.org/pdf/2306.01209v1.pdf | Counting Crowds in Bad Weather | Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding. Numerous methods have been proposed and achieved state-of-the-art performance for real-world tasks. However, existing approaches do not perform well under adverse weather such... | ['Ming-Hsuan Yang', 'Sy-Yen Kuo', 'Yuan-Chun Chiang', 'Wei-Ting Chen', 'Zhi-Kai Huang'] | 2023-06-02 | null | null | null | null | ['crowd-counting', 'image-restoration'] | ['computer-vision', 'computer-vision'] | [ 3.97054106e-02 -6.15751445e-01 4.96609271e-01 -4.57801491e-01
-1.77185744e-01 -3.23041320e-01 5.59925437e-01 5.76367155e-02
-8.21993172e-01 7.23157763e-01 -6.38284981e-02 6.92175478e-02
4.85806137e-01 -6.49494767e-01 -4.61249888e-01 -7.16002345e-01
3.04727882e-01 4.20939058e-01 7.75367379e-01 -2.36102626... | [8.436019897460938, -0.3567752540111542] |
c27ab0a7-0f98-4dbe-bc92-a337ef9f29ba | biocpt-contrastive-pre-trained-transformers | 2307.00589 | null | https://arxiv.org/abs/2307.00589v1 | https://arxiv.org/pdf/2307.00589v1.pdf | BioCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval | Information retrieval (IR) is essential in biomedical knowledge acquisition and clinical decision support. While recent progress has shown that language model encoders perform better semantic retrieval, training such models requires abundant query-article annotations that are difficult to obtain in biomedicine. As a re... | ['Zhiyong Lu', 'John Wilbur', 'Lana Yeganova', 'Donald C. Comeau', 'Qingyu Chen', 'Won Kim', 'Qiao Jin'] | 2023-07-02 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'retrieval', 'semantic-retrieval', 'information-retrieval'] | ['computer-vision', 'methodology', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [ 4.38915998e-01 1.74493954e-01 -6.81323171e-01 -2.88617551e-01
-1.65164840e+00 -2.30068922e-01 3.40899229e-01 6.84701443e-01
-6.57262146e-01 8.09641778e-01 5.15907586e-01 -4.26839948e-01
-1.91647232e-01 -5.14545381e-01 -7.28838384e-01 -3.76681477e-01
3.12734872e-01 9.88065481e-01 -1.56580508e-02 -2.02671126... | [8.603961944580078, 8.735188484191895] |
298b44c4-1979-4fc8-8203-2c72de80c506 | cyber-secure-teleoperation-with-encrypted | 2302.13709 | null | https://arxiv.org/abs/2302.13709v1 | https://arxiv.org/pdf/2302.13709v1.pdf | Cyber-Secure Teleoperation With Encrypted Four-Channel Bilateral Control | This study developed an encrypted four-channel bilateral control system that enables posture synchronization and force feedback for leader and follower robot arms. The encrypted bilateral control system communicates encrypted signals and operates with encrypted control parameters using homomorphic encryption. We create... | ['Kiminao Kogiso', 'Kenichi Abe', 'Toru Mizuya', 'Kaoru Teranishi', 'Akane Kosugi', 'Haruki Takanashi'] | 2023-02-27 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.17719308e-01 1.49600282e-01 -1.18161105e-01 2.25883052e-01
2.25494727e-01 -1.22364378e+00 9.07326162e-01 -2.34879777e-01
-5.22651613e-01 8.07005465e-01 -2.77475476e-01 -5.73461652e-01
2.22346440e-01 -8.97232592e-01 -7.62705028e-01 -7.77734995e-01
-5.66574097e-01 -1.71434712e-02 -2.34380737e-01 -3.48461658... | [5.185304164886475, 2.786564588546753] |
55ba1d76-8c3e-466c-8b81-a15d4f4ba708 | deep-stable-representation-learning-on | 2209.01321 | null | https://arxiv.org/abs/2209.01321v1 | https://arxiv.org/pdf/2209.01321v1.pdf | Deep Stable Representation Learning on Electronic Health Records | Deep learning models have achieved promising disease prediction performance of the Electronic Health Records (EHR) of patients. However, most models developed under the I.I.D. hypothesis fail to consider the agnostic distribution shifts, diminishing the generalization ability of deep learning models to Out-Of-Distribut... | ['Qiang Liu', 'Zhaocheng Liu', 'Yingtao Luo'] | 2022-09-03 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-6.57838881e-02 2.09310651e-01 -2.84957737e-01 -5.29228389e-01
-5.39816439e-01 -2.56003052e-01 5.05076170e-01 2.17436180e-01
-3.91506962e-02 8.00749779e-01 6.72560930e-01 -5.65966785e-01
-5.66179693e-01 -9.20029819e-01 -8.24533045e-01 -7.87936509e-01
-2.98359483e-01 2.26683319e-01 -3.50925237e-01 1.40234157... | [7.955477237701416, 5.9806694984436035] |
4028112d-f288-4af5-88a6-a7086afc2e1b | improving-molecular-pretraining-with | 2209.15101 | null | https://arxiv.org/abs/2209.15101v1 | https://arxiv.org/pdf/2209.15101v1.pdf | Improving Molecular Pretraining with Complementary Featurizations | Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery. Recently, prosperous progress has been made in molecular pretraining with different molecular featurizations, including 1D... | ['Shu Wu', 'Qiang Liu', 'Yingze Wang', 'Yuanqi Du', 'Dingshuo Chen', 'Yanqiao Zhu'] | 2022-09-29 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 5.03698587e-01 -2.83568144e-01 -7.83912063e-01 -3.59091908e-01
-1.86521441e-01 -8.56946051e-01 6.77267432e-01 5.34506857e-01
-2.54200757e-01 1.22589231e+00 1.24249503e-01 -1.02410531e+00
-4.02058288e-02 -7.49311805e-01 -9.38545287e-01 -8.15575182e-01
-3.25295269e-01 4.30306792e-02 -3.13688070e-01 -2.74988085... | [5.109690189361572, 5.817135334014893] |
d983ad64-39c6-4bb8-934b-36062f52c818 | urbanbis-a-large-scale-benchmark-for-fine | 2305.02627 | null | https://arxiv.org/abs/2305.02627v1 | https://arxiv.org/pdf/2305.02627v1.pdf | UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation | We present the UrbanBIS benchmark for large-scale 3D urban understanding, supporting practical urban-level semantic and building-level instance segmentation. UrbanBIS comprises six real urban scenes, with 2.5 billion points, covering a vast area of 10.78 square kilometers and 3,370 buildings, captured by 113,346 views ... | ['Hui Huang', 'Chi-Wing Fu', 'Ke Xie', 'Qi Zhang', 'Fuyou Xue', 'Guoqing Yang'] | 2023-05-04 | null | null | null | null | ['3d-reconstruction', 'autonomous-navigation'] | ['computer-vision', 'computer-vision'] | [-6.79487810e-02 -8.41507167e-02 4.75786850e-02 -3.98751855e-01
-8.67860138e-01 -5.89127600e-01 6.94758654e-01 6.78398758e-02
2.33688965e-01 4.61697161e-01 -8.74430239e-02 -3.58575583e-01
-4.75290716e-02 -1.87013710e+00 -7.44069695e-01 -3.99372220e-01
5.07390946e-02 1.15012693e+00 7.07384646e-01 -4.55250263... | [8.17943286895752, -2.7708892822265625] |
68ecec91-e477-4e36-9afb-08a9aa16106f | attentive-fusion-enhanced-audio-visual | 2008.02686 | null | https://arxiv.org/abs/2008.02686v1 | https://arxiv.org/pdf/2008.02686v1.pdf | Attentive Fusion Enhanced Audio-Visual Encoding for Transformer Based Robust Speech Recognition | Audio-visual information fusion enables a performance improvement in speech recognition performed in complex acoustic scenarios, e.g., noisy environments. It is required to explore an effective audio-visual fusion strategy for audiovisual alignment and modality reliability. Different from the previous end-to-end approa... | ['Li-Rong Dai', 'JunFeng Hou', 'Jie Zhang', 'Liangfa Wei'] | 2020-08-06 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 3.28791201e-01 1.04194209e-01 3.38304430e-01 -3.28850389e-01
-1.35621345e+00 -2.69254446e-01 7.82718062e-01 2.11643443e-01
-5.02262831e-01 3.20233196e-01 4.56644654e-01 -1.22162186e-01
-6.69207647e-02 -1.98473841e-01 -6.27276659e-01 -8.27635646e-01
2.89719582e-01 -1.51739985e-01 2.27782056e-01 -5.27072996... | [14.401000022888184, 5.194441795349121] |
3b0f9272-b5cb-4290-8217-2c83b28b1cab | ino-at-factify-2-structure-coherence-based | 2303.0151 | null | https://arxiv.org/abs/2303.01510v1 | https://arxiv.org/pdf/2303.01510v1.pdf | INO at Factify 2: Structure Coherence based Multi-Modal Fact Verification | This paper describes our approach to the multi-modal fact verification (FACTIFY) challenge at AAAI2023. In recent years, with the widespread use of social media, fake news can spread rapidly and negatively impact social security. Automatic claim verification becomes more and more crucial to combat fake news. In fact ve... | ['Tongyue Wang', 'Xi Wang', 'Zhulin Tao', 'Yinuo Zhang'] | 2023-03-02 | null | null | null | null | ['fact-verification', 'semantic-textual-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.19854310e-02 -2.00699747e-01 -3.83960843e-01 -2.40075335e-01
-1.03805208e+00 -6.88577414e-01 9.68868792e-01 6.29142702e-01
-1.88882411e-01 7.00643480e-01 6.09025300e-01 -2.49879465e-01
8.65355581e-02 -7.26179481e-01 -4.59537089e-01 -3.71855050e-01
2.93821603e-01 6.52166381e-02 3.77679706e-01 -5.02906978... | [8.169697761535645, 10.262467384338379] |
f54a97ff-2203-4405-8d5e-bbc00d047987 | domain-independent-turn-level-dialogue | 1908.07064 | null | https://arxiv.org/abs/1908.07064v1 | https://arxiv.org/pdf/1908.07064v1.pdf | Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimation | An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and rely on annotation schemes with low ... | ['Spyros Matsoukas', 'Praveen Kumar Bodigutla', 'Joshua Levy', 'Alborz Geramifard', 'Swanand Joshi', 'Longshaokan Wang', 'Kate Ridgeway'] | 2019-08-19 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-1.44984171e-01 5.48659563e-01 -2.24500462e-01 -1.11185706e+00
-1.12248313e+00 -5.30006349e-01 3.70368689e-01 2.94143885e-01
-6.24931037e-01 1.12978947e+00 6.96369827e-01 -8.01405609e-02
1.37858471e-04 -2.42566288e-01 3.26763034e-01 5.17745130e-02
2.23296613e-01 6.58096850e-01 -1.03939220e-01 -7.59463787... | [12.872467994689941, 8.067788124084473] |
5d9451a4-354e-47fb-92e1-d3799c1b9575 | bootstrap-aggregation-and-confidence-measures | 2306.08946 | null | https://arxiv.org/abs/2306.08946v1 | https://arxiv.org/pdf/2306.08946v1.pdf | Bootstrap aggregation and confidence measures to improve time series causal discovery | Causal discovery methods have demonstrated the ability to identify the time series graphs representing the causal temporal dependency structure of dynamical systems. However, they do not include a measure of the confidence of the estimated links. Here, we introduce a novel bootstrap aggregation (bagging) and confidence... | ['Veronika Eyring', 'Andreas Gerhardus', 'Jakob Runge', 'Kevin Debeire'] | 2023-06-15 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-8.82527307e-02 6.73649609e-02 -1.73739329e-01 1.80518582e-01
-2.39418268e-01 -5.69721222e-01 8.59798312e-01 5.10586679e-01
-8.08497425e-03 1.33474398e+00 8.71114135e-02 -5.36680222e-01
-6.81149840e-01 -1.18794334e+00 -5.70405006e-01 -7.57729471e-01
-9.34037507e-01 5.32973707e-01 5.68834186e-01 3.25956494... | [7.669063568115234, 5.212095737457275] |
32906ccf-3d33-4569-b320-57750086b52a | benchmarking-the-combinatorial | 2109.08925 | null | https://arxiv.org/abs/2109.08925v2 | https://arxiv.org/pdf/2109.08925v2.pdf | Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs | Complex Query Answering (CQA) is an important reasoning task on knowledge graphs. Current CQA learning models have been shown to be able to generalize from atomic operators to more complex formulas, which can be regarded as the combinatorial generalizability. In this paper, we present EFO-1-QA, a new dataset to benchma... | ['Yangqiu Song', 'Hang Yin', 'ZiHao Wang'] | 2021-09-18 | null | null | null | null | ['complex-query-answering'] | ['knowledge-base'] | [-1.14989303e-01 1.44808918e-01 -1.59230292e-01 -3.07820201e-01
-4.40156043e-01 -8.28083396e-01 8.04045379e-01 3.97282094e-01
-2.29955226e-01 4.14835215e-01 8.12021866e-02 -6.62895024e-01
-4.24208969e-01 -1.22122014e+00 -7.38897085e-01 -1.66071355e-01
-3.58646423e-01 1.04230213e+00 5.98560750e-01 -7.04365492... | [9.37146282196045, 7.639078617095947] |
885feff6-2b32-4af2-a58e-6588e8478518 | retrieval-augmented-visual-question-answering | 2210.03809 | null | https://arxiv.org/abs/2210.03809v2 | https://arxiv.org/pdf/2210.03809v2.pdf | Retrieval Augmented Visual Question Answering with Outside Knowledge | Outside-Knowledge Visual Question Answering (OK-VQA) is a challenging VQA task that requires retrieval of external knowledge to answer questions about images. Recent OK-VQA systems use Dense Passage Retrieval (DPR) to retrieve documents from external knowledge bases, such as Wikipedia, but with DPR trained separately f... | ['Bill Byrne', 'Weizhe Lin'] | 2022-10-07 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-2.25036800e-01 5.14928391e-03 1.59602556e-02 -1.33887291e-01
-1.48271453e+00 -9.37728524e-01 5.41197002e-01 2.63663214e-02
-4.44839299e-01 5.43610573e-01 3.07057351e-01 -2.69285202e-01
4.65850607e-02 -8.35496187e-01 -8.82769763e-01 -4.52532709e-01
4.25985545e-01 5.44519126e-01 4.50583279e-01 -2.74704933... | [10.864771842956543, 1.6535120010375977] |
3a538d3d-99db-4255-a6ca-7e58b738981b | assorted-archetypal-and-annotated-two-million | 2303.16778 | null | https://arxiv.org/abs/2303.16778v1 | https://arxiv.org/pdf/2303.16778v1.pdf | Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning | Cooking recipes allow individuals to exchange culinary ideas and provide food preparation instructions. Due to a lack of adequate labeled data, categorizing raw recipes found online to the appropriate food genres is a challenging task in this domain. Utilizing the knowledge of domain experts to categorize recipes could... | ['Hasan Mahmud', 'Md. Kamrul Hasan', 'Md. Mohsinul Kabir', 'G. M. Shahariar', 'Nazmus Sakib'] | 2023-03-27 | null | null | null | null | ['genre-classification', 'recipe-generation', 'semantic-role-labeling', 'part-of-speech-tagging'] | ['computer-vision', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [-2.72731483e-01 6.41577840e-02 -5.61405003e-01 -5.19088089e-01
-9.18441534e-01 -1.15160787e+00 1.88350901e-01 8.82828832e-01
-2.83888131e-01 6.64471567e-01 6.50494874e-01 2.17206720e-02
1.97423026e-01 -1.01243830e+00 -4.97299612e-01 -7.74097860e-01
4.32742298e-01 3.58679205e-01 -3.57713588e-02 -1.56070799... | [11.527652740478516, 4.542551517486572] |
f19dfe0d-f53f-46c6-a407-446a576a2399 | seeing-in-extra-darkness-using-a-deep-red | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xiong_Seeing_in_Extra_Darkness_Using_a_Deep-Red_Flash_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xiong_Seeing_in_Extra_Darkness_Using_a_Deep-Red_Flash_CVPR_2021_paper.pdf | Seeing in Extra Darkness Using a Deep-Red Flash | We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source. Our main observation is that in a dim environment, the human eye mainly uses rods for the perception of light, which are not sensitive to wavelengths longer than 620nm, yet the camera sensor still has a spectral ... | ['Shree Nayar', 'Wolfgang Heidrich', 'Jian Wang', 'Jinhui Xiong'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['video-reconstruction'] | ['computer-vision'] | [ 4.66877073e-01 -5.44065416e-01 3.05137306e-01 -1.48753211e-01
-3.46196666e-02 -3.35470766e-01 1.91811264e-01 -8.70387495e-01
-7.14276671e-01 9.06323433e-01 2.22472772e-02 -2.27112338e-01
3.89553934e-01 -5.66536129e-01 -7.47685075e-01 -9.97602105e-01
5.24657369e-01 -6.61399603e-01 4.26889956e-01 -1.02057442... | [10.700254440307617, -2.4702224731445312] |
66f4793b-9961-4478-a174-638037b79c65 | act-the-part-learning-interaction-strategies | 2105.01047 | null | https://arxiv.org/abs/2105.01047v1 | https://arxiv.org/pdf/2105.01047v1.pdf | Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery | People often use physical intuition when manipulating articulated objects, irrespective of object semantics. Motivated by this observation, we identify an important embodied task where an agent must play with objects to recover their parts. To this end, we introduce Act the Part (AtP) to learn how to interact with arti... | ['Shuran Song', 'Kiana Ehsani', 'Samir Yitzhak Gadre'] | 2021-05-03 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Gadre_Act_the_Part_Learning_Interaction_Strategies_for_Articulated_Object_Part_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Gadre_Act_the_Part_Learning_Interaction_Strategies_for_Articulated_Object_Part_ICCV_2021_paper.pdf | iccv-2021-1 | ['motion-segmentation', 'physical-intuition'] | ['computer-vision', 'reasoning'] | [ 3.11695933e-01 7.06123590e-01 -8.90292823e-02 -5.26729859e-02
-4.83152151e-01 -1.03787899e+00 4.52423513e-01 -1.19361408e-01
-1.32741928e-01 6.22570872e-01 1.64833888e-01 1.46948621e-01
-4.55648787e-02 -6.35714531e-01 -1.05929494e+00 -5.46511114e-01
-7.11212680e-02 8.60805213e-01 3.68376046e-01 1.01986350... | [5.031423568725586, 0.263572633266449] |
e56971d9-8773-411a-908b-d88347c3385c | planverb-domain-independent-verbalization-and | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/21204 | https://ojs.aaai.org/index.php/AAAI/article/view/21204/20953 | PlanVerb: Domain-Independent Verbalization and Summary of Task Plans | For users to trust planning algorithms, they must be able to understand the planner's outputs and the reasons for each action selection. This output does not tend to be user-friendly, often consisting of sequences of parametrised actions or task networks. And these may not be practical for non-expert users who may find... | ['Andrew Coles', 'Paul Luff', 'Senka Krivic ́', 'Gerard Canal'] | 2022-06-28 | null | null | null | proceedings-of-the-aaai-conference-on-5 | ['robot-navigation'] | ['robots'] | [ 2.88151771e-01 1.13088322e+00 -1.11791417e-02 -7.40740657e-01
-3.42378110e-01 -6.52404308e-01 7.05050349e-01 3.41062576e-01
-1.91373155e-01 9.47598815e-01 8.26463521e-01 -5.48886418e-01
-3.63924235e-01 -7.90200472e-01 -2.97372490e-01 -1.30733877e-01
-1.66284159e-01 1.04578328e+00 5.56420922e-01 -5.51045477... | [4.401830673217773, 1.030422568321228] |
d2627b24-db3c-49dd-8153-305b889f1c46 | all-in-sam-from-weak-annotation-to-pixel-wise | 2307.0029 | null | https://arxiv.org/abs/2307.00290v1 | https://arxiv.org/pdf/2307.00290v1.pdf | All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning | The Segment Anything Model (SAM) is a recently proposed prompt-based segmentation model in a generic zero-shot segmentation approach. With the zero-shot segmentation capacity, SAM achieved impressive flexibility and precision on various segmentation tasks. However, the current pipeline requires manual prompts during th... | ['Yuankai Huo', 'Yucheng Tang', 'Lucas W. Remedios', 'Shunxing Bao', 'Tianyuan Yao', 'Quan Liu', 'Ruining Deng', 'Can Cui'] | 2023-07-01 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 6.52045786e-01 4.56988990e-01 -1.26094058e-01 -3.88155103e-01
-1.12603509e+00 -4.98876333e-01 2.15607762e-01 2.67958403e-01
-7.28016734e-01 5.02223134e-01 -2.63035715e-01 -2.86487669e-01
1.56582266e-01 -5.74134707e-01 -5.40298164e-01 -8.03323448e-01
5.35682738e-01 6.82850361e-01 8.27642143e-01 -6.04204275... | [14.585158348083496, -2.177517890930176] |
8de6816f-1a81-4c28-b0c2-b2dd02dbb169 | structure-function-dynamics-hybrid-modeling | 2305.03925 | null | https://arxiv.org/abs/2305.03925v3 | https://arxiv.org/pdf/2305.03925v3.pdf | Structure-Function Dynamics Hybrid Modeling: RNA Degradation | RNA structure and functional dynamics play fundamental roles in controlling biological systems. Molecular dynamics simulation, which can characterize interactions at an atomistic level, can advance the understanding on new drug discovery, manufacturing, and delivery mechanisms. However, it is computationally unattainab... | ['Wandi Xu', 'Chunsheng Fang', 'Ailun Wang', 'Paul Whitford', 'Wei Xie', 'Hua Zheng'] | 2023-05-06 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 1.69996798e-01 -3.85227263e-01 -3.34179044e-01 2.68757015e-01
1.08741699e-02 -8.87820423e-01 7.33092785e-01 4.07483190e-01
1.07967548e-01 1.15811121e+00 1.23699695e-01 -6.76392913e-01
-3.39844227e-01 -4.86181170e-01 -6.51185572e-01 -1.27459228e+00
-4.35820445e-02 3.95440578e-01 -1.54647961e-01 -5.49303591... | [5.706139087677002, 4.478531360626221] |
c4294213-c811-4cfa-afc8-b4923d536d16 | text-augmentation-in-a-multi-task-view | 2101.05469 | null | https://arxiv.org/abs/2101.05469v1 | https://arxiv.org/pdf/2101.05469v1.pdf | Text Augmentation in a Multi-Task View | Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training. In this paper, we propose an alternative perspective -- a multi-task view (MTV) of data augmenta... | ['Soroush Vosoughi', 'Shiqi Xu', 'Chengyu Huang', 'Jason Wei'] | 2021-01-14 | null | https://aclanthology.org/2021.eacl-main.252 | https://aclanthology.org/2021.eacl-main.252.pdf | eacl-2021-2 | ['text-augmentation'] | ['natural-language-processing'] | [ 6.74751103e-01 5.31938910e-01 -5.75001657e-01 -5.05355477e-01
-7.68851697e-01 -3.19848537e-01 7.65466332e-01 3.46077681e-01
-5.77377439e-01 1.03560746e+00 3.28937829e-01 -3.40248704e-01
1.29380286e-01 -6.23125494e-01 -7.94874489e-01 -5.00446558e-01
2.27475941e-01 7.87670851e-01 -1.23137459e-01 -3.00733298... | [10.821576118469238, 8.101004600524902] |
50be851b-c947-4203-8aae-4cffba225b2a | atomai-a-deep-learning-framework-for-analysis | 2105.07485 | null | https://arxiv.org/abs/2105.07485v1 | https://arxiv.org/pdf/2105.07485v1.pdf | AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond | AtomAI is an open-source software package bridging instrument-specific Python libraries, deep learning, and simulation tools into a single ecosystem. AtomAI allows direct applications of the deep convolutional neural networks for atomic and mesoscopic image segmentation converting image and spectroscopy data into class... | ['Sergei V. Kalinin', 'Tommy Wong', 'Ayana Ghosh', 'Maxim Ziatdinov'] | 2021-05-16 | null | null | null | null | ['materials-imaging', 'im2spec'] | ['computer-vision', 'computer-vision'] | [ 2.19646394e-01 -3.19371700e-01 1.24313697e-01 -3.73517036e-01
-6.52151883e-01 -5.29251635e-01 5.73655307e-01 3.44793022e-01
-3.98399651e-01 8.78026068e-01 -9.49741155e-02 -5.57318330e-01
-6.29938692e-02 -8.60278487e-01 -7.25082755e-01 -1.11860466e+00
-1.83083966e-01 7.41289318e-01 -1.20841451e-01 7.89432228... | [5.275662899017334, 5.553041934967041] |
a50422ae-ea98-4066-9c69-43276b7e816e | learning-to-borrow-relation-representation-2 | null | null | https://aclanthology.org/2022.naacl-main.209 | https://aclanthology.org/2022.naacl-main.209.pdf | Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion | Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences in text corpora. Such sentences (textual mentions of entity-pairs) are represented as Lexicalised Dependency Paths (LDPs) between two entit... | ['Danushka Bollegala', 'Angrosh Mandya', 'Mona Hakami', 'Huda Hakami'] | null | null | null | null | naacl-2022-7 | ['entity-embeddings'] | ['methodology'] | [-2.19760820e-01 6.68240845e-01 -3.42246115e-01 -1.84386283e-01
-2.16785163e-01 -6.20789170e-01 6.19674623e-01 9.08022702e-01
-5.84408581e-01 1.04627502e+00 5.18866658e-01 -4.68255907e-01
-3.91799092e-01 -1.18413055e+00 -8.39739561e-01 -1.86462298e-01
-3.92566621e-01 5.09856403e-01 5.91596484e-01 -4.25975204... | [9.275588035583496, 8.589839935302734] |
0a784663-8d11-4bde-bb57-112ff3eab428 | topological-data-analysis-for-word-sense | 2203.00565 | null | https://arxiv.org/abs/2203.00565v1 | https://arxiv.org/pdf/2203.00565v1.pdf | Topological Data Analysis for Word Sense Disambiguation | We develop and test a novel unsupervised algorithm for word sense induction and disambiguation which uses topological data analysis. Typical approaches to the problem involve clustering, based on simple low level features of distance in word embeddings. Our approach relies on advanced mathematical concepts in the field... | ['Rishabh Choudhary', 'Mithun Bharadwaj', 'Samuel Dooley', 'Michael Rawson'] | 2022-03-01 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 2.45772004e-02 2.37570539e-01 -1.39476329e-01 -2.93314427e-01
-2.56400973e-01 -6.58648431e-01 7.69404948e-01 9.37904954e-01
-7.76473224e-01 5.32527268e-01 3.50801557e-01 -5.84741354e-01
-5.26163459e-01 -1.10652483e+00 -2.26078555e-01 -8.29970181e-01
-6.95294797e-01 6.91549718e-01 4.75864112e-01 -7.37777650... | [10.210132598876953, 8.741706848144531] |
68241f40-b2c3-4247-b717-a494bfe5bff8 | towards-shared-datasets-for-normalization | null | null | https://aclanthology.org/L14-1574 | https://aclanthology.org/L14-1574.pdf | Towards Shared Datasets for Normalization Research | In this paper we present a Dutch and English dataset that can serve as a gold standard for evaluating text normalization approaches. With the combination of text messages, message board posts and tweets, these datasets represent a variety of user generated content. All data was manually normalized to their standard for... | ["V{\\'e}ronique Hoste", "Orph{\\'e}e De Clercq", 'Sarah Schulz', 'Bart Desmet'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['lexical-normalization'] | ['natural-language-processing'] | [ 4.25664276e-01 9.97374877e-02 -4.03332829e-01 -5.36867380e-01
-9.85587299e-01 -6.58689082e-01 8.23867202e-01 1.01744771e+00
-1.15417993e+00 9.61717069e-01 8.45309973e-01 -3.42180699e-01
1.07164010e-01 -6.94171607e-01 -1.19089104e-01 -1.65457845e-01
4.82782215e-01 4.03071016e-01 3.23069543e-02 -6.69939935... | [10.011420249938965, 9.960016250610352] |
3e6fdec0-3923-4c3f-ac90-20955a9fce0a | whats-wrong-with-hebrew-nlp-and-how-to-make | 1908.05453 | null | https://arxiv.org/abs/1908.05453v1 | https://arxiv.org/pdf/1908.05453v1.pdf | What's Wrong with Hebrew NLP? And How to Make it Right | For languages with simple morphology, such as English, automatic annotation pipelines such as spaCy or Stanford's CoreNLP successfully serve projects in academia and the industry. For many morphologically-rich languages (MRLs), similar pipelines show sub-optimal performance that limits their applicability for text anal... | ['Amit Seker', 'Reut Tsarfaty', 'Stav Klein', 'Shoval Sadde'] | 2019-08-15 | whats-wrong-with-hebrew-nlp-and-how-to-make-1 | https://aclanthology.org/D19-3044 | https://aclanthology.org/D19-3044.pdf | ijcnlp-2019-11 | ['morphological-disambiguation'] | ['natural-language-processing'] | [-1.35728538e-01 1.39190882e-01 -7.73941651e-02 -3.59114230e-01
-1.09243309e+00 -1.07926762e+00 3.17790270e-01 6.92306459e-01
-6.65926576e-01 7.11807311e-01 2.84465313e-01 -6.04649127e-01
-9.69044119e-02 -5.94118237e-01 -1.27322942e-01 -2.75600731e-01
2.03263745e-01 7.23473728e-01 1.68654978e-01 -2.04134136... | [10.452557563781738, 10.093512535095215] |
79f48f01-79f0-44df-aa44-da7fa89aa298 | what-makes-a-question-inquisitive-a-study-on-1 | null | null | https://aclanthology.org/2022.starsem-1.22 | https://aclanthology.org/2022.starsem-1.22.pdf | “What makes a question inquisitive?” A Study on Type-Controlled Inquisitive Question Generation | We propose a type-controlled framework for inquisitive question generation. We annotate an inquisitive question dataset with question types, train question type classifiers, and finetune models for type-controlled question generation. Empirical results demonstrate that we can generate a variety of questions that adhere... | ['Kevin Gimpel', 'Debanjan Ghosh', 'Lingyu Gao'] | null | null | null | null | sem-naacl-2022-7 | ['question-generation', 'question-selection'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.98731348e-01 6.68856382e-01 2.42458880e-01 -4.97768313e-01
-1.62802064e+00 -1.12620115e+00 8.23159099e-01 2.82094419e-01
-4.30857211e-01 7.57517934e-01 5.64491212e-01 -5.09655535e-01
-2.93917537e-01 -6.12339258e-01 -3.29079747e-01 -4.05317023e-02
5.45232832e-01 7.76339293e-01 5.39418042e-01 -5.29155433... | [11.62270736694336, 8.181865692138672] |
61677587-795e-48d6-932f-e22f7a64d186 | coherent-comments-generation-for-chinese | null | null | https://aclanthology.org/P19-1479 | https://aclanthology.org/P19-1479.pdf | Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model | Automatic article commenting is helpful in encouraging user engagement on online news platforms. However, the news documents are usually too long for models under traditional encoder-decoder frameworks, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a graph-t... | ['Xu sun', 'Yunfang Wu', 'Yancheng He', 'Jingjing Xu', 'Wei Li', 'ShengLi Yan'] | 2019-07-01 | null | null | null | acl-2019-7 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 9.69235078e-02 8.46421242e-01 -5.37507772e-01 -3.00992221e-01
-8.17204475e-01 -4.55087662e-01 7.32998133e-01 2.62470216e-01
1.01125956e-01 8.35946321e-01 1.43717802e+00 -4.37116414e-01
7.50444770e-01 -5.91071665e-01 -6.03501379e-01 -3.52348015e-02
2.44284853e-01 3.52966636e-01 6.19572662e-02 -6.50428295... | [12.328950881958008, 9.247425079345703] |
e24ea9b7-aba3-45a6-a283-64f6f7687c7d | shadingnet-image-intrinsics-by-fine-grained | 1912.04023 | null | https://arxiv.org/abs/1912.04023v3 | https://arxiv.org/pdf/1912.04023v3.pdf | ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition | In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, ... | ['Theo Gevers', 'Hoang-An Le', 'Sezer Karaoglu', 'Anil S. Baslamisli', 'Partha Das'] | 2019-12-09 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.50922877e-01 -3.61864924e-01 6.67786598e-01 -6.95001841e-01
-4.22351241e-01 -4.35488671e-01 7.06948161e-01 -3.76976430e-01
-2.81361956e-02 7.47954309e-01 3.01150918e-01 -1.02688290e-01
1.91549361e-01 -9.85108018e-01 -6.48525357e-01 -1.15650344e+00
1.71586022e-01 1.64455563e-01 1.56765133e-01 -3.63321632... | [9.876137733459473, -2.9776949882507324] |
47d40e17-55e8-448b-ac03-771ae5a8615a | per-example-gradient-regularization-improves | 2303.1794 | null | https://arxiv.org/abs/2303.17940v1 | https://arxiv.org/pdf/2303.17940v1.pdf | Per-Example Gradient Regularization Improves Learning Signals from Noisy Data | Gradient regularization, as described in \citet{barrett2021implicit}, is a highly effective technique for promoting flat minima during gradient descent. Empirical evidence suggests that this regularization technique can significantly enhance the robustness of deep learning models against noisy perturbations, while also... | ['Difan Zou', 'Yuan Cao', 'Xuran Meng'] | 2023-03-31 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.07521698e-01 -4.02255774e-01 5.57687581e-02 -3.42956692e-01
-7.74518788e-01 -4.86162215e-01 1.86584353e-01 7.36958459e-02
-5.82279682e-01 7.47088373e-01 -7.86036849e-02 -3.97025764e-01
-2.95021027e-01 -5.56837022e-01 -1.11828911e+00 -9.45128202e-01
1.66920319e-01 -4.10497725e-01 -2.71577686e-02 -1.99097306... | [8.455638885498047, 3.6047236919403076] |
690cc5fd-423c-4dbc-9f5e-8e542c52f4d3 | body-size-and-depth-disambiguation-in-multi | 2111.01884 | null | https://arxiv.org/abs/2111.01884v2 | https://arxiv.org/pdf/2111.01884v2.pdf | Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images | We address the problem of multi-person 3D body pose and shape estimation from a single image. While this problem can be addressed by applying single-person approaches multiple times for the same scene, recent works have shown the advantages of building upon deep architectures that simultaneously reason about all people... | ['Francesc Moreno-Noguer', 'Alberto Sanfeliu', 'Antonio Agudo', 'Adria Ruiz', 'Nicolas Ugrinovic'] | 2021-11-02 | null | null | null | null | ['3d-multi-person-mesh-recovery'] | ['computer-vision'] | [-6.56956658e-02 -2.80247480e-02 3.97307336e-01 -3.00668091e-01
-3.28155786e-01 -4.23451751e-01 4.64376390e-01 3.26403789e-02
-4.42653507e-01 4.04079765e-01 2.47093171e-01 4.89867359e-01
9.26065817e-03 -6.53371155e-01 -5.75688660e-01 -4.65484500e-01
1.17702357e-01 1.20750749e+00 1.86689645e-01 -2.63758153... | [7.088843822479248, -1.0639195442199707] |
42cd3201-ba31-42e8-8562-abba03dbe22d | skill-based-reinforcement-learning-with | 2210.07426 | null | https://arxiv.org/abs/2210.07426v4 | https://arxiv.org/pdf/2210.07426v4.pdf | Skill-Based Reinforcement Learning with Intrinsic Reward Matching | While unsupervised skill discovery has shown promise in autonomously acquiring behavioral primitives, there is still a large methodological disconnect between task-agnostic skill pretraining and downstream, task-aware finetuning. We present Intrinsic Reward Matching (IRM), which unifies these two phases of learning via... | ['Pieter Abbeel', 'Amber Xie', 'Ademi Adeniji'] | 2022-10-14 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 4.31039512e-01 -6.05636276e-02 -1.55618057e-01 -6.46688789e-02
-8.05144310e-01 -9.34109569e-01 4.83066767e-01 1.58092435e-02
-9.38079357e-01 1.05863202e+00 -4.41415235e-02 -2.60383099e-01
-5.96536219e-01 -4.48843777e-01 -7.68431306e-01 -7.82842219e-01
-2.66175628e-01 6.74036801e-01 8.87136087e-02 -3.10007691... | [4.1045145988464355, 1.6055314540863037] |
73f2fb92-2f53-4695-852a-d518f816e85b | neuro-causal-factor-analysis | 2305.19802 | null | https://arxiv.org/abs/2305.19802v1 | https://arxiv.org/pdf/2305.19802v1.pdf | Neuro-Causal Factor Analysis | Factor analysis (FA) is a statistical tool for studying how observed variables with some mutual dependences can be expressed as functions of mutually independent unobserved factors, and it is widely applied throughout the psychological, biological, and physical sciences. We revisit this classic method from the comparat... | ['Liam Solus', 'Bryon Aragam', 'MingYu Liu', 'Alex Markham'] | 2023-05-31 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 6.15471266e-02 2.21518204e-01 -4.24133837e-01 -2.39974350e-01
1.39356017e-01 -3.73095304e-01 1.16284060e+00 -1.66409597e-01
3.84669825e-02 8.70507538e-01 9.57983911e-01 -2.87069410e-01
-7.74452150e-01 -7.53786445e-01 -8.79444122e-01 -9.16586936e-01
-3.15184474e-01 6.33823276e-01 -3.23975354e-01 7.89339095... | [7.866450786590576, 5.19666862487793] |
697dc2fc-564d-4049-bfe7-a3f4bcb11f61 | multilevel-sentence-embeddings-for | 2305.05748 | null | https://arxiv.org/abs/2305.05748v1 | https://arxiv.org/pdf/2305.05748v1.pdf | Multilevel Sentence Embeddings for Personality Prediction | Representing text into a multidimensional space can be done with sentence embedding models such as Sentence-BERT (SBERT). However, training these models when the data has a complex multilevel structure requires individually trained class-specific models, which increases time and computing costs. We propose a two step a... | ['Masashi Morita', 'Akira Yuasa', 'Paolo Tirotta'] | 2023-05-09 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 4.50876355e-02 1.69442683e-01 -2.71285534e-01 -7.90676296e-01
-6.81491315e-01 -4.98197556e-01 6.86963081e-01 6.10209703e-01
-5.65576017e-01 7.06259310e-01 2.96959609e-01 -1.86033875e-01
-1.81671064e-02 -9.58527744e-01 -4.62205142e-01 -4.50531781e-01
1.42000571e-01 5.54748893e-01 8.60045105e-02 -3.34692568... | [10.845069885253906, 8.641457557678223] |
9b4c68b3-d0e8-4e45-bbdf-f4faaa7a41d6 | predicting-epileptic-seizures-using | null | null | https://www.medrxiv.org/content/10.1101/19000430v1 | https://www.medrxiv.org/content/medrxiv/early/2019/06/25/19000430.full.pdf | Predicting epileptic seizures using nonnegative matrix factorization | This paper presents a procedure for the patient-specific prediction of epileptic seizures. To this end, a combination of nonnegative matrix factorization (NMF) and smooth basis functions with robust regression is applied to power spectra of intracranial electroencephalographic (iEEG) signals. The resulting time and fre... | ['Olivera Stojanović', 'Gordon Pipa'] | 2019-06-25 | null | null | null | medrxiv-plos-one-under-review-2019-6 | ['seizure-prediction', 'epilepsy-prediction'] | ['medical', 'medical'] | [ 3.95657122e-01 -2.65388280e-01 8.03627223e-02 -2.21212685e-01
-4.61275399e-01 -3.13274413e-01 2.42872700e-01 2.23254561e-01
-2.18273804e-01 1.00570869e+00 3.25091034e-01 -6.92463443e-02
-5.50157666e-01 -8.37354213e-02 -9.03169736e-02 -1.05311918e+00
-6.27108991e-01 1.25726655e-01 -3.13889176e-01 -4.10555676... | [13.163576126098633, 3.493694305419922] |
971c5e68-8014-47b2-bdde-0441f72b9ea1 | graph-neural-networks-for-human-aware-social | 1909.09003 | null | https://arxiv.org/abs/1909.09003v3 | https://arxiv.org/pdf/1909.09003v3.pdf | Graph Neural Networks for Human-aware Social Navigation | Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to navigate avoiding going through the personal spaces of the people surrounding them. Complying with social rules such as not getting in the middle of human-to-human and human-to-object interactions is also important. ... | ['Ronit R. Jorvekar', 'Pablo Bustos', 'Diego R. Faria', 'Pilar Bachiller', 'Luis J. Manso'] | 2019-09-19 | null | null | null | null | ['social-navigation'] | ['robots'] | [-1.60816193e-01 6.98899388e-01 2.99375266e-01 -2.84818649e-01
5.01014769e-01 -2.02477112e-01 6.44373536e-01 1.44107014e-01
-7.85525382e-01 9.85738337e-01 1.38577700e-01 -4.29042071e-01
-5.54494083e-01 -7.85727799e-01 -6.04253054e-01 -3.24239582e-01
-3.95104289e-01 1.10961401e+00 4.50564981e-01 -7.39845335... | [4.840753078460693, 0.887368381023407] |
9a2bf920-fa19-41ef-8f7d-1c82d8c3a051 | proximal-policy-optimization-algorithms | 1707.06347 | null | http://arxiv.org/abs/1707.06347v2 | http://arxiv.org/pdf/1707.06347v2.pdf | Proximal Policy Optimization Algorithms | We propose a new family of policy gradient methods for reinforcement
learning, which alternate between sampling data through interaction with the
environment, and optimizing a "surrogate" objective function using stochastic
gradient ascent. Whereas standard policy gradient methods perform one gradient
update per data s... | ['John Schulman', 'Filip Wolski', 'Alec Radford', 'Oleg Klimov', 'Prafulla Dhariwal'] | 2017-07-20 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-2.8391546e-01 -1.3733596e-01 -7.2922450e-01 -1.1217749e-01
-6.1764562e-01 -4.6537629e-01 6.7141330e-01 4.0485926e-02
-1.0181148e+00 1.5481156e+00 1.8266396e-01 -5.7111293e-01
-1.5423280e-01 -5.0031793e-01 -7.9695308e-01 -7.1073341e-01
-4.0857351e-01 5.3257394e-01 2.5887477e-01 -4.6801910e-01
5.4791409e-01... | [4.107600212097168, 2.213590383529663] |
7973c2c5-f087-4f25-8096-91f826e5db4f | uni-qsar-an-auto-ml-tool-for-molecular | 2304.12239 | null | https://arxiv.org/abs/2304.12239v1 | https://arxiv.org/pdf/2304.12239v1.pdf | Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction | Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery. However, most DL based QSAR models are restricted to limited labeled data to achieve better performance, and also are sensiti... | ['Linfeng Zhang', 'Guolin Ke', 'Hang Zheng', 'Hongshuai Wang', 'Guojiang Zhao', 'Xiaohong Ji', 'Zhifeng Gao'] | 2023-04-24 | null | null | null | null | ['drug-discovery', 'molecular-property-prediction'] | ['medical', 'miscellaneous'] | [ 1.56085372e-01 -3.27488452e-01 -7.66958714e-01 -2.84946591e-01
-1.14859605e+00 -6.10542774e-01 2.51882404e-01 7.11508453e-01
-9.71606523e-02 1.31251359e+00 -8.22927250e-05 -7.35953033e-01
-3.51871014e-01 -6.09505951e-01 -8.93347263e-01 -8.80181551e-01
-5.65515757e-01 8.40771437e-01 1.91582069e-02 -5.35580777... | [5.151066780090332, 5.853537559509277] |
b9b02b72-0259-464e-a3b1-41fa70f1f104 | quickprobs-2-towards-rapid-construction-of | 1512.07437 | null | http://arxiv.org/abs/1512.07437v2 | http://arxiv.org/pdf/1512.07437v2.pdf | QuickProbs 2: towards rapid construction of high-quality alignments of large protein families | Increasing size of sequence databases caused by the development of high
throughput sequencing, poses multiple alignment algorithms to face one of the
greatest challenges yet. As we show, well-established techniques employed for
increasing alignment quality, i.e., refinement and consistency, are ineffective
when large p... | [] | 2016-08-30 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 3.65937978e-01 -3.33628595e-01 -1.80165708e-01 -1.39673382e-01
-7.82812893e-01 -6.32026255e-01 3.16809267e-01 6.86883986e-01
-5.45801282e-01 1.31017458e+00 -3.30628365e-01 -4.26441669e-01
-3.88911188e-01 -4.90579277e-01 -4.90458786e-01 -1.01611149e+00
-4.34596799e-02 1.27475393e+00 6.82021558e-01 -3.17915589... | [4.834841728210449, 5.231966972351074] |
77b0c695-5e46-4b52-b773-21e923cb1605 | a-high-performance-cnn-method-for-offline | 1812.11489 | null | https://arxiv.org/abs/1812.11489v2 | https://arxiv.org/pdf/1812.11489v2.pdf | A High-Performance CNN Method for Offline Handwritten Chinese Character Recognition and Visualization | Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR). However, many of them did not address the problem of network interpretability. We propose a new architecture of a deep CNN with high recognition performance which i... | ['Zhiqiang You', 'Pavlo Melnyk', 'Keqin Li'] | 2018-12-30 | null | null | null | null | ['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character'] | ['computer-vision', 'natural-language-processing'] | [ 1.23448670e-01 -1.86452851e-01 2.18067113e-02 -4.42109823e-01
-2.29702711e-01 -4.11901325e-01 3.80179435e-01 7.56242592e-03
-7.26188838e-01 5.36595464e-01 -1.86119959e-01 -5.03275275e-01
-4.20160824e-03 -8.21048915e-01 -7.01192439e-01 -6.30306363e-01
-1.87294669e-02 1.43449428e-02 3.33939642e-01 -7.56099150... | [11.760517120361328, 2.6227502822875977] |
833c9062-deb8-427f-a67e-a9e56f2ef28c | person-recognition-in-personal-photo-1 | 1509.03502 | null | http://arxiv.org/abs/1509.03502v2 | http://arxiv.org/pdf/1509.03502v2.pdf | Person Recognition in Personal Photo Collections | Recognising persons in everyday photos presents major challenges (occluded
faces, different clothing, locations, etc.) for machine vision. We propose a
convnet based person recognition system on which we provide an in-depth
analysis of informativeness of different body cues, impact of training data,
and the common fail... | ['Seong Joon Oh', 'Rodrigo Benenson', 'Mario Fritz', 'Bernt Schiele'] | 2015-09-11 | person-recognition-in-personal-photo-2 | http://openaccess.thecvf.com/content_iccv_2015/html/Oh_Person_Recognition_in_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Oh_Person_Recognition_in_ICCV_2015_paper.pdf | iccv-2015-12 | ['person-recognition'] | ['computer-vision'] | [ 2.23077741e-02 -1.42501146e-01 2.10232243e-01 -6.02668941e-01
7.57381395e-02 -2.88268209e-01 7.35166073e-01 -5.98561108e-01
-5.46547472e-01 5.01763284e-01 5.86373746e-01 4.74858701e-01
-9.85219851e-02 -4.39194173e-01 -4.89826173e-01 -4.02901471e-01
-1.96048930e-01 3.18391293e-01 2.34312378e-03 -2.05342054... | [14.254260063171387, 0.9424132704734802] |
2531ebf2-5a32-4192-ac61-77a9e55a2fcf | event-based-tracking-of-human-hands | 2304.06534 | null | https://arxiv.org/abs/2304.06534v1 | https://arxiv.org/pdf/2304.06534v1.pdf | Event-based tracking of human hands | This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand posit... | ['Pedro Neto', 'Mohammad Safeea', 'Laura Duarte'] | 2023-04-13 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [ 4.20570970e-01 -3.48536879e-01 3.04824561e-01 6.15141541e-03
-1.47218227e-01 -5.97270191e-01 3.44371259e-01 3.31194699e-01
-1.14822614e+00 6.36179030e-01 -2.68161995e-04 3.80260348e-01
-2.16083467e-01 -6.63055062e-01 -3.03117275e-01 -6.57630086e-01
-3.49877387e-01 2.29077071e-01 6.53356612e-01 1.31567687... | [8.263763427734375, -1.1700176000595093] |
868f2421-7796-4c31-9fac-4395f1f20cd4 | earthmapper-a-tool-box-for-the-semantic | 1804.00292 | null | http://arxiv.org/abs/1804.00292v1 | http://arxiv.org/pdf/1804.00292v1.pdf | EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery | Deep learning continues to push state-of-the-art performance for the semantic
segmentation of color (i.e., RGB) imagery; however, the lack of annotated data
for many remote sensing sensors (i.e. hyperspectral imagery (HSI)) prevents
researchers from taking advantage of this recent success. Since generating
sensor speci... | ['Christopher Kanan', 'Utsav B. Gewali', 'Ronald Kemker'] | 2018-04-01 | null | null | null | null | ['segmentation-of-remote-sensing-imagery', 'the-semantic-segmentation-of-remote-sensing'] | ['miscellaneous', 'miscellaneous'] | [ 5.59196174e-01 -2.94635266e-01 1.27133623e-01 -6.64106905e-01
-1.04629207e+00 -8.51798952e-01 2.58456349e-01 2.30745245e-02
-4.29508120e-01 6.25020444e-01 -2.69067675e-01 -9.96240735e-01
-3.31609994e-01 -1.19225419e+00 -5.79747260e-01 -8.02339554e-01
-3.37624431e-01 4.27785546e-01 8.11926126e-02 3.90374996... | [9.47828197479248, -1.4769662618637085] |
cc97f892-4bf8-4a24-a59c-27526589161c | emergence-of-a-phonological-bias-in-chatgpt | 2305.15929 | null | https://arxiv.org/abs/2305.15929v2 | https://arxiv.org/pdf/2305.15929v2.pdf | Emergence of a phonological bias in ChatGPT | Current large language models, such as OpenAI's ChatGPT, have captured the public's attention because how remarkable they are in the use of language. Here, I demonstrate that ChatGPT displays phonological biases that are a hallmark of human language processing. More concretely, just like humans, ChatGPT has a consonant... | ['Juan Manuel Toro'] | 2023-05-25 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-2.35875428e-01 1.67267203e-01 -1.63363963e-01 -2.14825854e-01
-7.78711438e-02 -8.17022800e-01 6.13825023e-01 3.44818026e-01
-5.17999768e-01 1.37162387e-01 5.71287334e-01 -6.00652277e-01
3.67708266e-01 -8.38010907e-01 -5.13109207e-01 -3.45926851e-01
1.52110577e-01 5.04267752e-01 1.66256577e-01 -5.23785233... | [10.525548934936523, 9.090611457824707] |
59736214-2d42-4af0-a15d-8874cdc9a392 | an-evaluation-of-large-scale-methods-for | 1708.02898 | null | http://arxiv.org/abs/1708.02898v1 | http://arxiv.org/pdf/1708.02898v1.pdf | An evaluation of large-scale methods for image instance and class discovery | This paper aims at discovering meaningful subsets of related images from
large image collections without annotations. We search groups of images related
at different levels of semantic, i.e., either instances or visual classes.
While k-means is usually considered as the gold standard for this task, we
evaluate and show... | ['Hervé Jégou', 'Matthijs Douze', 'Jeff Johnson'] | 2017-08-09 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 2.12376472e-02 4.76526879e-02 3.26494388e-02 -2.18166918e-01
-9.20791209e-01 -8.03762197e-01 7.95677602e-01 6.69537723e-01
-7.67029941e-01 5.72092175e-01 -6.46545887e-02 1.01485960e-01
-3.46588910e-01 -5.64648211e-01 -6.58505738e-01 -6.94232523e-01
-2.25045487e-01 1.05518293e+00 7.35778272e-01 2.47709215... | [9.450721740722656, 1.0346708297729492] |
11fa7de5-efd0-48d7-b0d5-00e888284d64 | an-event-calculus-production-rule-system-for | 1512.04358 | null | http://arxiv.org/abs/1512.04358v2 | http://arxiv.org/pdf/1512.04358v2.pdf | An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains | Action languages have emerged as an important field of Knowledge
Representation for reasoning about change and causality in dynamic domains.
This article presents Cerbere, a production system designed to perform online
causal, temporal and epistemic reasoning based on the Event Calculus. The
framework implements the de... | ['Yacine Amirat', 'Abdelghani Chibani', 'Dimitris Plexousakis', 'Theodore Patkos'] | 2015-12-14 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-3.99378352e-02 6.36272371e-01 -1.59523800e-01 -3.11706632e-01
5.71353212e-02 -5.46142876e-01 1.35751283e+00 2.43224859e-01
-1.24622323e-02 9.81559396e-01 3.72097552e-01 -4.26031500e-01
-9.47605908e-01 -1.43797612e+00 -4.93041098e-01 -2.68041015e-01
-4.44834411e-01 5.37296414e-01 6.73608303e-01 -3.82693022... | [8.630037307739258, 6.660624980926514] |
9728496b-9723-4da7-8a38-d5f556361c97 | slotdiffusion-object-centric-generative | 2305.11281 | null | https://arxiv.org/abs/2305.11281v1 | https://arxiv.org/pdf/2305.11281v1.pdf | SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models | Object-centric learning aims to represent visual data with a set of object entities (a.k.a. slots), providing structured representations that enable systematic generalization. Leveraging advanced architectures like Transformers, recent approaches have made significant progress in unsupervised object discovery. In addit... | ['Animesh Garg', 'Igor Gilitschenski', 'Wuyue Lu', 'Jingyu Hu', 'Ziyi Wu'] | 2023-05-18 | null | null | null | null | ['object-discovery', 'unsupervised-object-segmentation', 'video-prediction', 'systematic-generalization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'reasoning'] | [ 1.82555839e-01 3.71627688e-01 -4.27678764e-01 -4.02298421e-01
-5.25562882e-01 -4.22471255e-01 1.18610954e+00 -2.54063845e-01
7.69591331e-02 5.38852155e-01 3.34473997e-01 1.24196394e-03
6.95758015e-02 -9.24917638e-01 -1.25970125e+00 -6.55350685e-01
5.41035309e-02 7.43459582e-01 2.59589434e-01 3.76397967... | [10.721420288085938, -0.209544375538826] |
9607d92c-f830-4063-ba9b-51e98c5b3edc | design-of-quantum-optical-experiments-with | 2109.13273 | null | https://arxiv.org/abs/2109.13273v3 | https://arxiv.org/pdf/2109.13273v3.pdf | Design of quantum optical experiments with logic artificial intelligence | Logic Artificial Intelligence (AI) is a subfield of AI where variables can take two defined arguments, True or False, and are arranged in clauses that follow the rules of formal logic. Several problems that span from physical systems to mathematical conjectures can be encoded into these clauses and solved by checking t... | ['Alán Aspuru-Guzik', 'Mario Krenn', 'Alba Cervera-Lierta'] | 2021-09-27 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 4.60269481e-01 5.42831540e-01 1.35665596e-01 -4.04429942e-01
-3.51529956e-01 -7.86455870e-01 4.56130385e-01 9.90760922e-02
-9.27945301e-02 1.01243675e+00 -4.19851154e-01 -8.13955009e-01
-6.67015254e-01 -1.25181651e+00 -7.89379478e-01 -6.44971907e-01
-4.05155532e-02 9.02000308e-01 1.36715904e-01 -2.72443891... | [8.583711624145508, 6.811710834503174] |
9534b3a7-e411-49fa-b742-e45201d904a1 | all-around-real-label-supervision-cyclic | 2109.1393 | null | https://arxiv.org/abs/2109.13930v2 | https://arxiv.org/pdf/2109.13930v2.pdf | All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation | Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially, the consistency-based approaches have attracted more attention for their superior performance, wherein the real labels are only utilized to... | ['Raymond Kai-yu Tong', 'Yefeng Zheng', 'Kai Ma', 'Jie Luo', 'Jiangpeng Yan', 'Lequan Yu', 'Donghuan Lu', 'Yixin Wang', 'Zhe Xu'] | 2021-09-28 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 5.26601493e-01 4.64595467e-01 -6.37751937e-01 -6.77325964e-01
-8.58235121e-01 -2.95654535e-01 4.28077012e-01 6.94248006e-02
-3.73605967e-01 7.55401969e-01 -1.78672716e-01 -3.34127963e-01
-3.58254552e-01 -3.69038403e-01 -5.93552530e-01 -1.29642296e+00
2.57821739e-01 5.76466978e-01 1.80008024e-01 9.21010077... | [14.664620399475098, -2.002408742904663] |
2bede581-e153-40ff-908d-5039fc6c3aa7 | pre-training-graph-neural-networks | 1905.12265 | null | https://arxiv.org/abs/1905.12265v3 | https://arxiv.org/pdf/1905.12265v3.pdf | Strategies for Pre-training Graph Neural Networks | Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and... | ['Jure Leskovec', 'Vijay Pande', 'Percy Liang', 'Bowen Liu', 'Marinka Zitnik', 'Joseph Gomes', 'Weihua Hu'] | 2019-05-29 | strategies-for-pre-training-graph-neural | https://openreview.net/forum?id=HJlWWJSFDH | https://openreview.net/pdf?id=HJlWWJSFDH | iclr-2020-1 | ['protein-function-prediction'] | ['medical'] | [ 6.98298931e-01 1.98444575e-01 -2.44531885e-01 -5.72228014e-01
-6.60675228e-01 -6.89624310e-01 5.40894210e-01 6.76515877e-01
-4.96006966e-01 7.45760381e-01 -1.85860917e-01 -6.19610012e-01
-1.56327337e-01 -9.63977337e-01 -8.46789002e-01 -6.51015282e-01
-1.84806466e-01 6.79796875e-01 2.28326023e-01 -3.41239423... | [6.792699813842773, 6.2642741203308105] |
2cb58307-4505-41f8-b3e0-281f8248112f | dpccn-densely-connected-pyramid-complex | 2112.1352 | null | https://arxiv.org/abs/2112.13520v2 | https://arxiv.org/pdf/2112.13520v2.pdf | DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation And Extraction | In recent years, a number of time-domain speech separation methods have been proposed. However, most of them are very sensitive to the environments and wide domain coverage tasks. In this paper, from the time-frequency domain perspective, we propose a densely-connected pyramid complex convolutional network, termed DPCC... | ['Jan Cernocky', 'Lukas Burget', 'Yanhua Long', 'Jiangyu Han'] | 2021-12-27 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 8.30669329e-02 -5.28452992e-01 2.11500287e-01 -1.66651145e-01
-1.02709007e+00 -6.26663327e-01 3.77408773e-01 -2.82299578e-01
-2.08361298e-01 6.64519489e-01 3.91804546e-01 -1.87965825e-01
-3.94673467e-01 -1.37096882e-01 -3.05259794e-01 -1.09564304e+00
4.06650230e-02 -7.34334886e-02 1.56041235e-01 -2.78411865... | [14.94832992553711, 5.937338352203369] |
cf76ea85-be00-43d2-95ce-c8f8220945c2 | syntax-guided-contrastive-learning-for-pre | null | null | https://aclanthology.org/2022.findings-acl.191 | https://aclanthology.org/2022.findings-acl.191.pdf | Syntax-guided Contrastive Learning for Pre-trained Language Model | Syntactic information has been proved to be useful for transformer-based pre-trained language models. Previous studies often rely on additional syntax-guided attention components to enhance the transformer, which require more parameters and additional syntactic parsing in downstream tasks. This increase in complexity s... | ['Hua Wu', 'Xinyan Xiao', 'Wang Lijie', 'Shuai Zhang'] | null | null | null | null | findings-acl-2022-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 6.78483173e-02 4.67748225e-01 -1.65390685e-01 -7.58682132e-01
-5.25556028e-01 -3.01109672e-01 1.16096959e-01 1.35166213e-01
-4.65263993e-01 3.67112011e-01 6.57128870e-01 -6.65870965e-01
2.57117212e-01 -8.27800989e-01 -6.44622743e-01 -3.38740379e-01
9.89030078e-02 3.36594552e-01 1.38497993e-01 -3.96986008... | [10.564558029174805, 9.307389259338379] |
80cf9479-2ea0-4fa3-8d6c-eddc27a0d249 | seeing-the-advantage-visually-grounding-word | 2202.10292 | null | https://arxiv.org/abs/2202.10292v1 | https://arxiv.org/pdf/2202.10292v1.pdf | Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge | Distributional semantic models capture word-level meaning that is useful in many natural language processing tasks and have even been shown to capture cognitive aspects of word meaning. The majority of these models are purely text based, even though the human sensory experience is much richer. In this paper we create v... | ['Mirjam Ernestus', 'Stefan L. Frank', 'Danny Merkx'] | 2022-02-21 | null | https://aclanthology.org/2022.cmcl-1.1 | https://aclanthology.org/2022.cmcl-1.1.pdf | cmcl-acl-2022-5 | ['learning-semantic-representations', 'word-similarity', 'grounded-language-learning'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-1.20111212e-01 8.11759755e-03 -7.92988762e-02 -2.25032181e-01
-4.03830588e-01 -7.53670275e-01 1.05023432e+00 8.58164608e-01
-8.76982987e-01 2.05212787e-01 9.66884434e-01 -3.04238856e-01
8.79034773e-02 -8.73646438e-01 -2.74808109e-01 -5.32081127e-01
-1.46868797e-02 1.47352904e-01 9.93339866e-02 -4.68028784... | [10.5823392868042, 2.0908122062683105] |
e6dfa26a-4062-45d9-9680-da468493fd39 | a-novel-approach-for-automatic-acoustic | null | null | https://ieeexplore.ieee.org/abstract/document/7178320 | https://ieeexplore.ieee.org/abstract/document/7178320 | A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks | Acoustic novelty detection aims at identifying abnormal/novel acoustic signals which differ from the reference/normal data that the system was trained with. In this paper we present a novel unsupervised approach based on a denoising autoencoder. In our approach auditory spectral features are processed by a denoising au... | ['Erik Marchi ; Fabio Vesperini ; Florian Eyben ; Stefano Squartini ; Björn Schuller'] | 2015-08-06 | null | null | null | 2015-ieee-international-conference-on-1 | ['acoustic-novelty-detection'] | ['audio'] | [ 2.33820736e-01 -2.86580138e-02 8.09935808e-01 -2.66105324e-01
-8.41069102e-01 5.36850188e-03 2.66210020e-01 2.94460088e-01
-6.23285830e-01 3.94838929e-01 4.60512668e-01 2.54085928e-01
1.16052464e-01 -6.96775019e-01 -8.29324126e-01 -5.90534925e-01
-2.37600073e-01 1.70519575e-02 3.04182738e-01 -2.81945974... | [15.222040176391602, 5.4056549072265625] |
5540e62e-cfbe-4aeb-8add-1a4888c810df | body-meshes-as-points | 2105.02467 | null | https://arxiv.org/abs/2105.02467v2 | https://arxiv.org/pdf/2105.02467v2.pdf | Body Meshes as Points | We consider the challenging multi-person 3D body mesh estimation task in this work. Existing methods are mostly two-stage based--one stage for person localization and the other stage for individual body mesh estimation, leading to redundant pipelines with high computation cost and degraded performance for complex scene... | ['Jiashi Feng', 'Xuecheng Nie', 'Jun Hao Liew', 'Dongdong Yu', 'Jianfeng Zhang'] | 2021-05-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Body_Meshes_as_Points_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Body_Meshes_as_Points_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-6.58624172e-02 3.63563932e-02 2.88838660e-03 -2.15071291e-01
-9.23863649e-01 -1.63761288e-01 2.13351741e-01 1.59145087e-01
-1.04243927e-01 2.66484946e-01 8.35725293e-02 5.13701439e-01
2.38928407e-01 -1.02841115e+00 -6.96888387e-01 -3.69870484e-01
-7.38701075e-02 1.02613139e+00 6.31429911e-01 -8.15877318... | [7.121395587921143, -1.0045301914215088] |
36a02863-6189-4c1d-bfa2-f1ce0553f0cd | large-scale-multi-class-and-hierarchical | null | null | https://aclanthology.org/C16-1051 | https://aclanthology.org/C16-1051.pdf | Large-scale Multi-class and Hierarchical Product Categorization for an E-commerce Giant | In order to organize the large number of products listed in e-commerce sites, each product is usually assigned to one of the multi-level categories in the taxonomy tree. It is a time-consuming and difficult task for merchants to select proper categories within thousands of options for the products they sell. In this wo... | ['Koji Murakami', 'Ali Cevahir'] | 2016-12-01 | large-scale-multi-class-and-hierarchical-1 | https://aclanthology.org/C16-1051 | https://aclanthology.org/C16-1051.pdf | coling-2016-12 | ['product-categorization'] | ['miscellaneous'] | [-2.74570018e-01 -8.28945488e-02 -1.61922842e-01 -7.76275694e-01
-1.44083023e-01 -7.03346252e-01 1.06134832e-01 5.06302893e-01
-2.21573666e-01 3.52684349e-01 1.32597148e-01 -4.79143441e-01
-6.90774992e-02 -1.30278349e+00 -7.52003193e-01 -1.84075221e-01
-6.76592141e-02 9.37177062e-01 9.10832733e-02 -1.31030202... | [9.909576416015625, 6.15012788772583] |
cc91abf3-1048-4cc0-beae-fb656c5970ba | calibrated-rgb-d-salient-object-detection | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.pdf | Calibrated RGB-D Salient Object Detection | Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD). This naturally leads to the incorporation of depth information in addition to the conventional RGB image as input, known as RGB-D SOD or depth-aware... | ['Li Cheng', 'Huchuan Lu', 'Yefeng Zheng', 'Kai Ma', 'Qi Bi', 'Shunyu Yao', 'Yongri Piao', 'Miao Zhang', 'Shuang Yu', 'Jingjing Li', 'Wei Ji'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.68181336e-01 1.51086897e-02 6.71049878e-02 -2.93340594e-01
-6.90101504e-01 -2.25563660e-01 6.55073762e-01 1.51442856e-01
-3.71999413e-01 5.18082678e-01 1.79286122e-01 -6.38321191e-02
-3.88736948e-02 -7.61611283e-01 -3.38237464e-01 -1.01816797e+00
5.23806274e-01 -1.20258540e-01 8.05155873e-01 -2.70738542... | [9.587766647338867, -0.9314788579940796] |
6e6776f0-ab58-443c-9378-ca605d59ccd0 | sigan-siamese-generative-adversarial-network | 1807.0837 | null | http://arxiv.org/abs/1807.08370v1 | http://arxiv.org/pdf/1807.08370v1.pdf | SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination | Despite generative adversarial networks (GANs) can hallucinate
photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they
cannot guarantee preserving the identities of hallucinated HR faces, making the
HR faces poorly recognizable. To address this problem, we propose a Siamese GAN
(SiGAN) to recons... | ['Weng-Tai Su', 'Chia-Wen Lin', 'Chih-Chung Hsu', 'Gene Cheung'] | 2018-07-22 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 2.89247513e-01 4.12901700e-01 3.55371177e-01 -3.94226998e-01
-9.22284365e-01 -4.86392349e-01 4.07432795e-01 -9.85216737e-01
2.06643149e-01 8.58473957e-01 1.90564170e-01 2.33082786e-01
4.03218120e-01 -8.20788383e-01 -8.64749134e-01 -9.59314883e-01
4.45962816e-01 3.89786333e-01 -5.91384470e-01 -4.35185619... | [12.722399711608887, 0.035900287330150604] |
f58deb3c-43ad-4e61-9460-66ecaa258fb1 | 190503277 | 1905.03277 | null | https://arxiv.org/abs/1905.03277v2 | https://arxiv.org/pdf/1905.03277v2.pdf | Handheld Multi-Frame Super-Resolution | Compared to DSLR cameras, smartphone cameras have smaller sensors, which limits their spatial resolution; smaller apertures, which limits their light gathering ability; and smaller pixels, which reduces their signal-to noise ratio. The use of color filter arrays (CFAs) requires demosaicing, which further degrades resol... | ['Chia-Kai Liang', 'Ignacio Garcia-Dorado', 'Marc Levoy', 'Manfred Ernst', 'Bartlomiej Wronski', 'Peyman Milanfar', 'Michael Krainin', 'Damien Kelly'] | 2019-05-08 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 6.35734439e-01 -5.09493947e-01 3.46826375e-01 -1.87354863e-01
-6.98448181e-01 -7.76745617e-01 3.19766998e-01 -3.74556810e-01
-5.94304144e-01 6.07253134e-01 1.37704104e-01 -3.26087058e-01
3.58274639e-01 -7.47030914e-01 -6.48918211e-01 -8.41354012e-01
5.19638002e-01 -3.64890188e-01 5.88646472e-01 -5.21828569... | [10.594950675964355, -2.523838520050049] |
f057cafc-5508-4a82-9ff7-1a662f64e25a | duformer-a-novel-architecture-for-power-line | 2304.05821 | null | https://arxiv.org/abs/2304.05821v1 | https://arxiv.org/pdf/2304.05821v1.pdf | DUFormer: A Novel Architecture for Power Line Segmentation of Aerial Images | Power lines pose a significant safety threat to unmanned aerial vehicles (UAVs) operating at low altitudes. However, detecting power lines in aerial images is challenging due to the small size of the foreground data (i.e., power lines) and the abundance of background information. To address this challenge, we propose D... | ['Jia Xu', 'ZhenPeng Bian', 'Yong Deng', 'Feng Qiao', 'Ting Li', 'Jianshu Chao', 'Qiang Zhang', 'Deyu An'] | 2023-04-12 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 3.03198993e-01 -2.86950707e-01 -2.85320163e-01 -9.53614637e-02
-4.82194662e-01 -8.92548203e-01 3.49908993e-02 -8.12566802e-02
-1.06879413e-01 4.21837986e-01 -5.40092289e-01 -2.78042555e-01
-9.15641114e-02 -1.01877952e+00 -5.17634273e-01 -8.12293649e-01
-2.84608006e-01 -1.92910418e-01 2.18187630e-01 3.77129056... | [8.865575790405273, -0.9285736680030823] |
a13e3bb5-46d9-467e-93a2-af40bde83d32 | 3d-gans-and-latent-space-a-comprehensive | 2304.03932 | null | https://arxiv.org/abs/2304.03932v1 | https://arxiv.org/pdf/2304.03932v1.pdf | 3D GANs and Latent Space: A comprehensive survey | Generative Adversarial Networks (GANs) have emerged as a significant player in generative modeling by mapping lower-dimensional random noise to higher-dimensional spaces. These networks have been used to generate high-resolution images and 3D objects. The efficient modeling of 3D objects and human faces is crucial in t... | ['Subhankar Mishra', 'Satya Pratheek Tata'] | 2023-04-08 | null | null | null | null | ['point-cloud-reconstruction', '3d-semantic-scene-completion'] | ['computer-vision', 'computer-vision'] | [ 2.64741659e-01 2.36618087e-01 1.65187821e-01 -1.52710050e-01
-5.85347354e-01 -6.67464495e-01 7.61810005e-01 -8.32577169e-01
4.02647197e-01 5.09582222e-01 3.28957677e-01 -1.92245990e-01
2.11124718e-01 -1.21892846e+00 -6.37848735e-01 -7.28820503e-01
3.34713459e-01 7.49719024e-01 -1.84936449e-01 -4.37037386... | [9.087139129638672, -3.588909149169922] |
e979dc1e-8eef-4172-9bc3-3cca9417eb94 | gashis-transformer-a-multi-scale-visual | 2104.14528 | null | https://arxiv.org/abs/2104.14528v7 | https://arxiv.org/pdf/2104.14528v7.pdf | GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection | In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images. GasHis-Transformer model consists of two key modules designed to extract global and local inform... | ['Shiliang Ai', 'Changhao Sun', 'Hongzan Sun', 'Md Rahaman', 'Xiaoyan Li', 'Ge Wang', 'Yixin Li', 'Marcin Grzegorzek', 'Wanli Liu', 'Weiming Hu', 'Chen Li', 'HaoYuan Chen'] | 2021-04-29 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [-2.20166475e-01 9.09903571e-02 -3.56657393e-02 2.57103652e-01
-9.71374154e-01 -3.32191885e-01 1.17480956e-01 3.64142060e-01
-4.48727220e-01 2.32305393e-01 -4.09065634e-02 -5.12811601e-01
2.14272976e-01 -9.55789804e-01 -1.43972859e-01 -1.24434829e+00
-2.95713961e-01 2.20915806e-02 5.24438262e-01 -1.73275545... | [15.116557121276855, -2.9407849311828613] |
6e431ae6-460c-4361-af5d-384758af3120 | topic-preserving-synthetic-news-generation-an | 2010.16324 | null | https://arxiv.org/abs/2010.16324v1 | https://arxiv.org/pdf/2010.16324v1.pdf | Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach | Nowadays, there exist powerful language models such as OpenAI's GPT-2 that can generate readable text and can be fine-tuned to generate text for a specific domain. Considering GPT-2, it cannot directly generate synthetic news with respect to a given topic and the output of the language model cannot be explicitly contro... | ['Huan Liu', 'Kai Shu', 'Ahmadreza Mosallanezhad'] | 2020-10-30 | null | null | null | null | ['news-generation'] | ['natural-language-processing'] | [ 2.34644815e-01 5.88796198e-01 -1.39215127e-01 1.08265337e-02
-1.03192651e+00 -6.37190104e-01 1.01249230e+00 -4.60976437e-02
-4.37153816e-01 1.02798522e+00 3.70519370e-01 -2.38094762e-01
6.18425608e-01 -1.39214277e+00 -1.23736680e+00 -5.84901571e-01
4.54535097e-01 9.01935399e-01 1.74324647e-01 -5.50845981... | [11.818798065185547, 9.187715530395508] |
50d818b2-ecbb-4b47-b04f-3ed995441f68 | deep-online-correction-for-monocular-visual | 2103.10029 | null | https://arxiv.org/abs/2103.10029v1 | https://arxiv.org/pdf/2103.10029v1.pdf | Deep Online Correction for Monocular Visual Odometry | In this work, we propose a novel deep online correction (DOC) framework for monocular visual odometry. The whole pipeline has two stages: First, depth maps and initial poses are obtained from convolutional neural networks (CNNs) trained in self-supervised manners. Second, the poses predicted by CNNs are further improve... | ['Qian Zhang', 'Hongmei Zhu', 'Wenming Meng', 'Xinggang Wang', 'Wei Sui', 'Jiaxin Zhang'] | 2021-03-18 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.52029538e-01 1.97418705e-01 -1.32279903e-01 -4.45757687e-01
-3.15995812e-01 -4.29181665e-01 5.28169870e-01 -9.38310623e-02
-7.16943145e-01 7.20021963e-01 -6.11340255e-02 -1.51397824e-01
3.64379525e-01 -7.32451200e-01 -9.86205459e-01 -3.08242410e-01
3.53767216e-01 5.68542004e-01 4.83853728e-01 -1.10771999... | [8.106269836425781, -2.2028937339782715] |
cde2016d-337c-461e-b0ff-e5a35bbe4fbd | deep-attention-guided-graph-clustering-with | 2111.05548 | null | https://arxiv.org/abs/2111.05548v3 | https://arxiv.org/pdf/2111.05548v3.pdf | Deep Attention-guided Graph Clustering with Dual Self-supervision | Existing deep embedding clustering works only consider the deepest layer to learn a feature embedding and thus fail to well utilize the available discriminative information from cluster assignments, resulting performance limitation. To this end, we propose a novel method, namely deep attention-guided graph clustering w... | ['Junhui Hou', 'Yuheng Jia', 'Hui Liu', 'Zhihao Peng'] | 2021-11-10 | null | null | null | null | ['deep-attention', 'graph-clustering', 'deep-attention'] | ['computer-vision', 'graphs', 'natural-language-processing'] | [-2.87487149e-01 -9.75076780e-02 -1.88361153e-01 -6.03404582e-01
-7.40697324e-01 -3.36033374e-01 5.70127547e-01 1.99552178e-01
-3.98120433e-01 9.47697088e-02 1.88011989e-01 6.48052767e-02
-5.25946915e-02 -6.69561923e-01 -8.28518450e-01 -1.04133677e+00
-1.12905018e-01 3.62735629e-01 1.57598719e-01 4.98834401... | [8.994607925415039, 3.4272983074188232] |
ea689694-d979-4e31-845a-9fad226852f5 | learning-rich-features-from-rgb-d-images-for | 1407.5736 | null | http://arxiv.org/abs/1407.5736v1 | http://arxiv.org/pdf/1407.5736v1.pdf | Learning Rich Features from RGB-D Images for Object Detection and Segmentation | In this paper we study the problem of object detection for RGB-D images using
semantically rich image and depth features. We propose a new geocentric
embedding for depth images that encodes height above ground and angle with
gravity for each pixel in addition to the horizontal disparity. We demonstrate
that this geocen... | ['Pablo Arbeláez', 'Ross Girshick', 'Saurabh Gupta', 'Jitendra Malik'] | 2014-07-22 | null | null | null | null | ['object-detection-in-indoor-scenes'] | ['computer-vision'] | [ 5.65896332e-01 3.20006490e-01 -9.43490341e-02 -5.91868043e-01
-7.51039863e-01 -6.37377501e-01 3.64489377e-01 2.64960945e-01
-5.55685401e-01 1.46181673e-01 -2.64683455e-01 -2.11437955e-01
3.79783392e-01 -1.01860476e+00 -9.15201008e-01 -6.72709942e-01
-9.21071991e-02 4.58943069e-01 7.56086946e-01 8.79228339... | [7.917731761932373, -2.6384670734405518] |
a62721dd-ff72-4c8a-9812-dd3b848e82ee | aggression-identification-using-deep-learning | null | null | https://aclanthology.org/W18-4418 | https://aclanthology.org/W18-4418.pdf | Aggression Identification Using Deep Learning and Data Augmentation | Social media platforms allow users to share and discuss their opinions online. However, a minority of user posts is aggressive, thereby hinders respectful discussion, and {---} at an extreme level {---} is liable to prosecution. The automatic identification of such harmful posts is important, because it can support the... | ['Julian Risch', 'Ralf Krestel'] | 2018-08-01 | null | null | null | coling-2018-8 | ['aggression-identification'] | ['natural-language-processing'] | [-2.83272296e-01 3.20110500e-01 -5.93113750e-02 -4.73459244e-01
-7.73846686e-01 -4.96777713e-01 5.03327250e-01 6.52441680e-01
-9.44583237e-01 9.81453598e-01 3.79143834e-01 -4.82755542e-01
8.21515322e-02 -7.46933520e-01 -2.17023298e-01 -4.33734834e-01
1.61098123e-01 4.17048931e-01 -1.50560737e-01 -5.30713558... | [8.840527534484863, 10.426063537597656] |
e90e6f4a-4426-4549-9670-66c12fcc345b | unsupervised-3d-learning-for-shape-analysis | 2008.01068 | null | https://arxiv.org/abs/2008.01068v2 | https://arxiv.org/pdf/2008.01068v2.pdf | Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination | Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods. In this ... | ['Qian-Fang Zou', 'Yu-Qi Yang', 'Peng-Shuai Wang', 'Yang Liu', 'Xin Tong', 'Zhirong Wu'] | 2020-08-03 | null | null | null | null | ['3d-point-cloud-linear-classification'] | ['computer-vision'] | [-4.27666958e-03 -3.54979024e-03 -3.40912268e-02 -8.00182343e-01
-8.73647630e-01 -7.78825343e-01 4.71658826e-01 3.04694057e-01
-3.23955029e-01 1.32655680e-01 -1.78194761e-01 -1.33867621e-01
-2.48188078e-01 -9.53510165e-01 -6.84023023e-01 -5.31055093e-01
-1.59980189e-02 9.96573150e-01 3.37554753e-01 -3.64409131... | [7.980854034423828, -3.5369818210601807] |
a5b812ee-e992-466a-a0d4-1af1c06c717d | unsupervised-word-influencer-networks-from | null | null | https://aclanthology.org/W18-3109 | https://aclanthology.org/W18-3109.pdf | Unsupervised Word Influencer Networks from News Streams | In this paper, we propose a new unsupervised learning framework to use news events for predicting trends in stock prices. We present Word Influencer Networks (WIN), a graph framework to extract longitudinal temporal relationships between any pair of informative words from news streams. Using the temporal occurrence of ... | ['an', 'Sun Chakraborty', 'Ananth Balashankar', 'Lakshminarayanan Subramanian'] | 2018-07-01 | null | null | null | ws-2018-7 | ['relationship-extraction-distant-supervised', 'stock-price-prediction'] | ['natural-language-processing', 'time-series'] | [-3.85041326e-01 3.50024968e-01 -7.49542713e-01 -1.65031314e-01
-8.98453686e-03 -5.78480542e-01 1.15074408e+00 5.48935592e-01
4.90034148e-02 8.63335073e-01 9.38091755e-01 -5.93695283e-01
-5.15445948e-01 -1.39910889e+00 -7.52446175e-01 -3.46807897e-01
-8.86311889e-01 3.33202213e-01 4.90547061e-01 -2.95348853... | [9.008247375488281, 9.147653579711914] |
be37f5d1-c65d-4fe3-866d-c26f111204a5 | evm-cnn-real-time-contactless-heart-rate | 2212.13843 | null | https://arxiv.org/abs/2212.13843v1 | https://arxiv.org/pdf/2212.13843v1.pdf | EVM-CNN: Real-Time Contactless Heart Rate Estimation from Facial Video | With the increase in health consciousness, noninvasive body monitoring has aroused interest among researchers. As one of the most important pieces of physiological information, researchers have remotely estimated the heart rate (HR) from facial videos in recent years. Although progress has been made over the past few y... | ['Abdulmotaleb El Saddik', 'Haiwei Dong', 'Juan Arteaga-Falconi', 'Yang Liu', 'Ying Qiu'] | 2022-12-25 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 1.76968709e-01 -4.06415164e-02 2.06657276e-02 -4.53522831e-01
-2.47914955e-01 2.69879159e-02 2.52424300e-01 -6.45266175e-02
-5.01875579e-01 8.87449920e-01 1.77707836e-01 4.81602162e-01
-1.17060855e-01 -4.34825391e-01 -7.40715489e-02 -7.70922601e-01
-3.01906556e-01 -6.65703833e-01 -2.59840608e-01 6.71083108... | [13.883461952209473, 2.7614059448242188] |
698b4ee7-deae-4e29-bb14-5c67c58b79e0 | semi-supervised-meta-learning-for-cross | null | null | https://aclanthology.org/2021.metanlp-1.8 | https://aclanthology.org/2021.metanlp-1.8.pdf | Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification | Meta learning aims to optimize the model’s capability to generalize to new tasks and domains. Lacking a data-efficient way to create meta training tasks has prevented the application of meta-learning to the real-world few shot learning scenarios. Recent studies have proposed unsupervised approaches to create meta-train... | ['Jiong Zhang', 'Yue Li'] | null | null | null | null | acl-metanlp-2021-8 | ['cross-domain-few-shot'] | ['computer-vision'] | [ 4.39352185e-01 3.50302637e-01 -5.32915294e-01 -5.83720148e-01
-7.10054874e-01 -2.75145561e-01 7.80120671e-01 1.48541734e-01
-7.61479080e-01 9.34908628e-01 1.21886924e-01 -2.88031757e-01
-1.14649303e-01 -7.99323976e-01 -5.92957020e-01 -3.00047040e-01
2.25517422e-01 6.73277438e-01 1.11277819e-01 -5.97689927... | [10.034261703491211, 3.2375357151031494] |
5115de40-d8b6-44f1-8c09-080aa4b78443 | combining-public-human-activity-recognition | 2306.13735 | null | https://arxiv.org/abs/2306.13735v1 | https://arxiv.org/pdf/2306.13735v1.pdf | Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity | The use of supervised learning for Human Activity Recognition (HAR) on mobile devices leads to strong classification performances. Such an approach, however, requires large amounts of labeled data, both for the initial training of the models and for their customization on specific clients (whose data often differ great... | ['Claudio Bettini', 'Philippe Lalanda', 'François Portet', 'Gabriele Civitarese', 'Sannara Ek', 'Riccardo Presotto'] | 2023-06-23 | null | null | null | null | ['activity-recognition', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 1.27840176e-01 5.70328496e-02 -2.20719248e-01 -3.98959458e-01
-8.74939382e-01 -3.76175314e-01 3.81308019e-01 -3.98269035e-02
-4.97510254e-01 8.70887935e-01 -7.02824667e-02 -1.44747958e-01
-9.37830433e-02 -4.96255934e-01 -5.26091874e-01 -5.40673912e-01
2.56387860e-01 8.13363791e-01 4.09658015e-01 2.99657509... | [7.869289398193359, 1.2390934228897095] |
f1437fb7-308f-4e45-b003-e526ddca1215 | robust-subspace-segmentation-with-block | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Feng_Robust_Subspace_Segmentation_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Feng_Robust_Subspace_Segmentation_2014_CVPR_paper.pdf | Robust Subspace Segmentation with Block-diagonal Prior | The subspace segmentation problem is addressed in this paper by effectively constructing an exactly block-diagonal sample affinity matrix. The block-diagonal structure is heavily desired for accurate sample clustering but is rather difficult to obtain. Most current state-of-the-art subspace segmentation methods (such a... | ['Zhouchen Lin', 'Jiashi Feng', 'Shuicheng Yan', 'Huan Xu'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['face-clustering'] | ['computer-vision'] | [ 3.65286827e-01 -2.06707001e-01 -4.78401542e-01 -1.71527505e-01
-7.04092801e-01 -5.56209505e-01 2.53538489e-01 -5.20339489e-01
-7.57840499e-02 4.44003075e-01 1.48171380e-01 -3.87583017e-01
-3.87096912e-01 -1.56332374e-01 -4.56810385e-01 -1.07202327e+00
1.83766410e-01 4.29291934e-01 -4.34270389e-02 2.18018219... | [7.744099140167236, 4.485559463500977] |
e83c343e-f03f-4ffb-bd0b-0bd367a6d4f3 | an-intelligent-non-invasive-real-time-human | 2008.02567 | null | https://arxiv.org/abs/2008.02567v1 | https://arxiv.org/pdf/2008.02567v1.pdf | An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare | Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to... | ['Muhammad Ali Imran', 'Qammer H. Abbasi', 'Adnan Zahid', 'Kia Dashtipour', 'Syed Aziz Shah', 'William Taylor'] | 2020-08-06 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 4.12195444e-01 -1.22926040e-02 -1.62042633e-01 -1.00571908e-01
-3.42968494e-01 -2.58459657e-01 -1.99600961e-02 4.16972471e-04
-3.94880861e-01 8.07409108e-01 2.53423452e-01 -2.00213611e-01
-3.46981257e-01 -8.01381052e-01 -1.18901595e-01 -8.35607886e-01
-3.26990962e-01 1.60281464e-01 4.42601472e-01 -2.88584054... | [7.113077640533447, 0.5872757434844971] |
ea20745a-f1b1-4b89-9c17-3d9d5674592a | generative-adversarial-nets-from-a-density | 1610.0292 | null | http://arxiv.org/abs/1610.02920v2 | http://arxiv.org/pdf/1610.02920v2.pdf | Generative Adversarial Nets from a Density Ratio Estimation Perspective | Generative adversarial networks (GANs) are successful deep generative models.
GANs are based on a two-player minimax game. However, the objective function
derived in the original motivation is changed to obtain stronger gradients when
learning the generator. We propose a novel algorithm that repeats the density
ratio e... | ['Kotaro Nakayama', 'Yutaka Matsuo', 'Masahiro Suzuki', 'Masatoshi Uehara', 'Issei Sato'] | 2016-10-10 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-1.44628629e-01 2.80520737e-01 -1.62823871e-01 -2.62206882e-01
-7.85881996e-01 -4.68605101e-01 6.96573198e-01 -6.11371338e-01
-1.90827399e-01 1.30720115e+00 1.26389295e-01 -2.44677700e-02
8.94836709e-02 -1.12819648e+00 -7.00054824e-01 -9.87230599e-01
3.02688867e-01 6.95616841e-01 -2.09145233e-01 -3.04655284... | [11.62996768951416, -0.09579156339168549] |
7ed4e999-0833-41d2-8ea2-95b631ae16f2 | combining-shallow-and-deep-representations | null | null | https://aclanthology.org/2021.alta-1.7 | https://aclanthology.org/2021.alta-1.7.pdf | Combining Shallow and Deep Representations for Text-Pair Classification | Text-pair classification is the task of determining the class relationship between two sentences. It is embedded in several tasks such as paraphrase identification and duplicate question detection. Contemporary methods use fine-tuned transformer encoder semantic representations of the classification token in the text-p... | ['Zhenchang Xing', 'Sarvnaz Karimi', 'Vincent Nguyen'] | null | null | null | null | alta-2021-12 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 5.58151066e-01 3.86847258e-01 -1.35632798e-01 -7.74543405e-01
-1.12377524e+00 -5.15743196e-01 2.91074842e-01 3.83035690e-01
-2.94712842e-01 3.43877524e-01 4.20097739e-01 -5.83397388e-01
2.08440855e-01 -7.47092426e-01 -8.19240510e-01 1.55788586e-02
3.10436368e-01 3.40076149e-01 2.66730785e-01 -1.63292900... | [11.13060188293457, 8.733381271362305] |
6e4e2223-ffaa-4fb6-a65e-6369740a0695 | graph-neural-network-for-video-query-based | 2007.09877 | null | https://arxiv.org/abs/2007.09877v2 | https://arxiv.org/pdf/2007.09877v2.pdf | Graph Neural Network for Video Relocalization | In this paper, we focus on video relocalization task, which uses a query video clip as input to retrieve a semantic relative video clip in another untrimmed long video. we find that in video relocalization datasets, there exists a phenomenon showing that there does not exist consistent relationship between feature simi... | ['Yuan Zhou', 'Mingfei Wang', 'Ruolin Wang', 'Shuwei Huo'] | 2020-07-20 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 4.50539067e-02 -4.82650608e-01 -3.67835641e-01 -2.34815016e-01
-2.67676324e-01 -3.76460940e-01 4.43892717e-01 2.07701176e-01
-4.96160179e-01 5.28095245e-01 5.11244714e-01 1.51648447e-01
-1.89839303e-01 -4.95879680e-01 -7.44076431e-01 -6.90907896e-01
7.01681301e-02 -2.60819465e-01 6.20788932e-01 5.05656824... | [9.829684257507324, 0.7595773339271545] |
6f1e5f54-905e-4b0b-82d0-94244933748d | guess-where-actor-supervision-for | 1804.01824 | null | http://arxiv.org/abs/1804.01824v1 | http://arxiv.org/pdf/1804.01824v1.pdf | Guess Where? Actor-Supervision for Spatiotemporal Action Localization | This paper addresses the problem of spatiotemporal localization of actions in
videos. Compared to leading approaches, which all learn to localize based on
carefully annotated boxes on training video frames, we adhere to a
weakly-supervised solution that only requires a video class label. We introduce
an actor-supervise... | ['Victor Escorcia', 'Cuong D. Dao', 'Cees Snoek', 'Mihir Jain', 'Bernard Ghanem'] | 2018-04-05 | null | null | null | null | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 1.49958283e-01 4.42047030e-01 -4.08405662e-01 -4.22841042e-01
-7.55065262e-01 -4.65859830e-01 9.05020595e-01 -1.60428405e-01
-5.55268943e-01 3.59800190e-01 5.58181345e-01 4.24313664e-01
2.04995289e-01 -1.11921299e-02 -1.00871181e+00 -5.96979260e-01
-3.94924521e-01 5.18303633e-01 6.17550194e-01 1.17315151... | [8.358779907226562, 0.5888506174087524] |
01b3ed21-6cff-43e9-a63d-f06331bf488b | divgan-towards-diverse-paraphrase-generation | null | null | https://aclanthology.org/2020.findings-emnlp.218 | https://aclanthology.org/2020.findings-emnlp.218.pdf | DivGAN: Towards Diverse Paraphrase Generation via Diversified Generative Adversarial Network | Paraphrases refer to texts that convey the same meaning with different expression forms. Traditional seq2seq-based models on paraphrase generation mainly focus on the fidelity while ignoring the diversity of outputs. In this paper, we propose a deep generative model to generate diverse paraphrases. We build our model b... | ['Xiaojun Wan', 'Yue Cao'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 2.84119487e-01 7.48779103e-02 -9.81456265e-02 -3.87725383e-01
-8.76644731e-01 -7.89812505e-01 5.86905360e-01 -2.83315897e-01
5.24465479e-02 8.87836277e-01 8.54704857e-01 -1.67851578e-02
3.75420153e-01 -8.93705904e-01 -8.63438427e-01 -4.93380129e-01
6.60525143e-01 1.86660454e-01 -3.32935959e-01 -3.48360449... | [11.757858276367188, 9.301290512084961] |
67a9b2df-d564-4fd6-9e79-7c5e658a543d | sparsistent-model-discovery | 2106.11936 | null | https://arxiv.org/abs/2106.11936v2 | https://arxiv.org/pdf/2106.11936v2.pdf | Sparsistent Model Discovery | Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific fields. However, it remains an open question to know when model discovery algorithms based on sparse regression can actually recover the underlyi... | ['Remy Kusters', 'Gert-Jan Both', 'Georges Tod'] | 2021-06-22 | sparsistent-model-discovery-1 | https://openreview.net/forum?id=WNTscnQd1s | https://openreview.net/pdf?id=WNTscnQd1s | null | ['model-discovery'] | ['miscellaneous'] | [ 1.78759217e-01 -1.98402748e-01 3.60230297e-01 1.61916539e-01
-7.65906274e-01 -5.54010093e-01 5.60635507e-01 -5.06998189e-02
1.91008560e-02 9.80987549e-01 -1.00390971e-01 -2.18882263e-01
-6.59612358e-01 -5.05385756e-01 -6.82340026e-01 -1.29264772e+00
-3.11325282e-01 7.73074508e-01 -2.33547434e-01 3.28760631... | [6.571328639984131, 3.57417631149292] |
6f3c5525-2511-49ab-bb8f-7e54e833b898 | in-domain-self-supervised-learning-can-lead | 2307.01645 | null | https://arxiv.org/abs/2307.01645v1 | https://arxiv.org/pdf/2307.01645v1.pdf | In-Domain Self-Supervised Learning Can Lead to Improvements in Remote Sensing Image Classification | Self-supervised learning (SSL) has emerged as a promising approach for remote sensing image classification due to its ability to leverage large amounts of unlabeled data. In contrast to traditional supervised learning, SSL aims to learn representations of data without the need for explicit labels. This is achieved by f... | ['Dragi Kocev', 'Nikola Simidjievski', 'Ivan Kitanovski', 'Ivica Dimitrovski'] | 2023-07-04 | null | null | null | null | ['self-supervised-learning', 'scene-classification', 'remote-sensing-image-classification'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 6.67931199e-01 9.79174823e-02 -3.54498833e-01 -7.50199080e-01
-5.83125532e-01 -6.42900467e-01 7.51163781e-01 -3.13695855e-02
-3.89770180e-01 7.30641305e-01 -3.68985301e-03 -6.06857061e-01
-1.30215079e-01 -8.86806130e-01 -6.42635465e-01 -6.42329872e-01
-1.11773377e-02 4.90486979e-01 -4.28901874e-02 -3.89807224... | [9.673974990844727, -1.3678369522094727] |
f4454001-2192-4a7b-8972-02b8ccbe6b8b | road-planning-for-slums-via-deep | 2305.1306 | null | https://arxiv.org/abs/2305.13060v3 | https://arxiv.org/pdf/2305.13060v3.pdf | Road Planning for Slums via Deep Reinforcement Learning | Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustainable development of cities. Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums,... | ['Yong Li', 'Depeng Jin', 'Jingtao Ding', 'Hongyuan Su', 'Yu Zheng'] | 2023-05-22 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-9.81178805e-02 3.64866614e-01 -3.42845798e-01 -1.52672395e-01
-3.94031167e-01 -2.76016116e-01 4.66849357e-01 1.05587542e-01
-1.07138596e-01 9.60527480e-01 4.53273594e-01 -9.87263381e-01
-1.45762235e-01 -1.64886224e+00 -6.23471677e-01 -3.20803642e-01
-1.95278630e-01 5.86329758e-01 2.22921327e-01 -8.23994577... | [8.804935455322266, -1.5079439878463745] |
e2cc4094-d085-43ba-b902-4a237c068292 | trecvid-2019-an-evaluation-campaign-to | 2009.09984 | null | https://arxiv.org/abs/2009.09984v1 | https://arxiv.org/pdf/2009.09984v1.pdf | TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval | The TREC Video Retrieval Evaluation (TRECVID) 2019 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last nineteen ... | ['Lukas Diduch', 'Keith Curtis', 'Jesse Zhang', 'Asad A. Butt', 'Andrew Delgado', 'Wessel Kraaij', 'Afzal Godil', 'Yooyoung Lee', 'George Awad', 'Eliot Godard', 'Yvette Graham', 'Jonathan Fiscus', 'Georges Quenot', 'Alan F. Smeaton'] | 2020-09-21 | null | null | null | null | ['instance-search', 'ad-hoc-video-search'] | ['computer-vision', 'computer-vision'] | [ 2.39875048e-01 -7.84159064e-01 -1.42734334e-01 -3.61434191e-01
-1.64870095e+00 -9.96105790e-01 9.12525833e-01 5.18966794e-01
-9.82486010e-01 3.67331117e-01 4.79906201e-01 1.09346069e-01
-1.38503641e-01 -9.06991065e-02 -2.65230715e-01 -2.91968495e-01
-1.63017854e-01 2.27206782e-01 5.87519050e-01 -6.14844486... | [10.471197128295898, 0.6665819883346558] |
704e135c-c5f6-4624-a70b-6bff16ffce60 | jnr-joint-based-neural-rig-representation-for | 2007.06755 | null | https://arxiv.org/abs/2007.06755v3 | https://arxiv.org/pdf/2007.06755v3.pdf | JNR: Joint-based Neural Rig Representation for Compact 3D Face Modeling | In this paper, we introduce a novel approach to learn a 3D face model using a joint-based face rig and a neural skinning network. Thanks to the joint-based representation, our model enjoys some significant advantages over prior blendshape-based models. First, it is very compact such that we are orders of magnitude smal... | ['HsiangTao Wu', 'Mitch Rundle', 'Noranart Vesdapunt', 'Baoyuan Wang'] | 2020-07-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2989_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630375.pdf | eccv-2020-8 | ['3d-face-modeling'] | ['computer-vision'] | [-3.69218038e-03 7.43370771e-01 1.16244763e-01 -2.73275673e-01
-4.89927173e-01 -6.44699454e-01 4.85476702e-01 -7.33513653e-01
2.98071429e-02 3.83287877e-01 1.33491354e-02 -1.75687388e-01
2.92750925e-01 -6.90621912e-01 -8.66888881e-01 -2.96274692e-01
1.89034715e-01 5.02370059e-01 3.29101309e-02 -2.45203465... | [13.024069786071777, -0.11276587098836899] |
2be97b01-6d41-4573-afae-22024defd908 | equipocket-an-e-3-equivariant-geometric-graph | 2302.12177 | null | https://arxiv.org/abs/2302.12177v1 | https://arxiv.org/pdf/2302.12177v1.pdf | EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction | Predicting the binding sites of the target proteins plays a fundamental role in drug discovery. Most existing deep-learning methods consider a protein as a 3D image by spatially clustering its atoms into voxels and then feed the voxelized protein into a 3D CNN for prediction. However, the CNN-based methods encounter se... | ['Zhaohan Ding', 'Ye Yuan', 'Zhewei Wei', 'Wenbing Huang', 'Yang Zhang'] | 2023-02-23 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 1.10450082e-01 1.20079324e-01 -5.93789369e-02 -2.52092123e-01
-4.27203566e-01 -2.50749797e-01 1.87144950e-01 2.51118451e-01
-1.35933444e-01 5.73657751e-01 6.77968562e-02 -3.68928432e-01
1.57973289e-01 -7.34227359e-01 -1.26055801e+00 -1.05001426e+00
-6.66393116e-02 7.92034268e-01 3.54760975e-01 -1.20110735... | [4.979401111602783, 5.841747760772705] |
b59502b2-d107-4dc0-a2e8-db708afb9fbc | dtw-k-means-clustering-for-fault-detection-in | 2306.08003 | null | https://arxiv.org/abs/2306.08003v1 | https://arxiv.org/pdf/2306.08003v1.pdf | DTW k-means clustering for fault detection in photovoltaic modules | The increase in the use of photovoltaic (PV) energy in the world has shown that the useful life and maintenance of a PV plant directly depend on theability to quickly detect severe faults on a PV plant. To solve this problem of detection, data based approaches have been proposed in the literature.However, these previou... | ['Corinne Alonso', 'Marko Pavlov', 'Audine Subias', 'Louise Travé-Massuyès', 'Edgar Hernando Sepúlveda Oviedo'] | 2023-06-13 | null | null | null | null | ['clustering', 'fault-detection', 'dynamic-time-warping'] | ['methodology', 'miscellaneous', 'time-series'] | [ 1.61280125e-01 -8.53162557e-02 1.89933032e-01 5.80486879e-02
-2.88971961e-01 -1.03375387e+00 5.75324774e-01 5.68342030e-01
2.58883506e-01 7.53339589e-01 -3.62795055e-01 -1.60249338e-01
-6.75369143e-01 -9.38410103e-01 -1.15244046e-01 -1.19762278e+00
-9.11004767e-02 5.02446711e-01 5.46212077e-01 -1.19071975... | [6.886842250823975, 2.252988576889038] |
5d62480d-be15-4c73-924e-ec343be0785e | point-4d-transformer-networks-for-spatio | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Fan_Point_4D_Transformer_Networks_for_Spatio-Temporal_Modeling_in_Point_Cloud_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Fan_Point_4D_Transformer_Networks_for_Spatio-Temporal_Modeling_in_Point_Cloud_CVPR_2021_paper.pdf | Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos | Point cloud videos exhibit irregularities and lack of order along the spatial dimension where points emerge inconsistently across different frames. To capture the dynamics in point cloud videos, point tracking is usually employed. However, as points may flow in and out across frames, computing accurate point trajec... | ['Mohan Kankanhalli', 'Yi Yang', 'Hehe Fan'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['3d-human-action-recognition'] | ['computer-vision'] | [-1.41106740e-01 -5.49220324e-01 6.61548749e-02 -1.02368630e-01
-3.06105465e-01 -7.11519122e-01 4.97306734e-01 1.17010228e-01
-7.30714276e-02 5.60889654e-02 -4.05433744e-01 -1.51610643e-01
2.03235313e-01 -6.12341940e-01 -1.22145212e+00 -5.63955903e-01
-1.02855667e-01 3.16552520e-01 5.28602898e-01 7.83077478... | [8.462647438049316, -2.0235283374786377] |
a046d8fa-8130-4c1b-8de7-4706755027bc | prediction-of-properties-of-metal-alloy | 2109.09394 | null | https://arxiv.org/abs/2109.09394v1 | https://arxiv.org/pdf/2109.09394v1.pdf | Prediction of properties of metal alloy materials based on machine learning | Density functional theory and its optimization algorithm are the main methods to calculate the properties in the field of materials. Although the calculation results are accurate, it costs a lot of time and money. In order to alleviate this problem, we intend to use machine learning to predict material properties. In t... | ['Jie Hu', 'Yan Yang', 'Yongquan Jiang', 'Houchen Zuo'] | 2021-09-20 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [-4.99365330e-01 -3.21259975e-01 -2.95248628e-01 -3.10275525e-01
-4.05393660e-01 4.75457191e-01 2.90203486e-02 -4.93174717e-02
-2.36796439e-01 1.12488663e+00 -1.33079752e-01 -7.25972801e-02
-5.61293736e-02 -1.34950054e+00 -4.40644652e-01 -1.01748908e+00
1.56916767e-01 5.75594664e-01 1.67308092e-01 -3.65571529... | [5.29987096786499, 5.423282623291016] |
3df1a8d1-721f-4686-90d9-e8d055ba705c | deep-ensembling-for-perceptual-image-quality | 2305.09141 | null | https://arxiv.org/abs/2305.09141v1 | https://arxiv.org/pdf/2305.09141v1.pdf | Deep Ensembling for Perceptual Image Quality Assessment | Blind image quality assessment is a challenging task particularly due to the unavailability of reference information. Training a deep neural network requires a large amount of training data which is not readily available for image quality. Transfer learning is usually opted to overcome this limitation and different dee... | ['Atif Khan', 'Abdul Rauf Bhatti', 'H. M. Shahzad Asif', 'Nisar Ahmed'] | 2023-05-16 | null | null | null | null | ['blind-image-quality-assessment', 'image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [-2.09240556e-01 -5.13881624e-01 2.03322142e-01 -4.81536478e-01
-6.31667852e-01 -3.44263166e-01 4.57920671e-01 -1.00344382e-01
-6.34628534e-01 7.97270775e-01 3.89427431e-02 -1.68999508e-01
-2.36445650e-01 -8.68034303e-01 -6.07948244e-01 -5.51851153e-01
-2.37966329e-01 5.72594889e-02 6.60289377e-02 -1.59451216... | [11.822938919067383, -1.8514037132263184] |
c8fdf614-8df0-429c-ba87-09e3a5be3a65 | good-examples-make-a-faster-learner-simple | 2110.08454 | null | https://arxiv.org/abs/2110.08454v3 | https://arxiv.org/pdf/2110.08454v3.pdf | Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence. However, such methods may suffer... | ['Xiang Ren', 'Toshiyuki Sekiya', 'Ryosuke Mitani', 'Takashi Shibuya', 'Xinyu Feng', 'Kangmin Tan', 'Akshen Kadakia', 'Jay Pujara', 'Mahak Agarwal', 'Dong-Ho Lee'] | 2021-10-16 | null | https://aclanthology.org/2022.acl-long.192 | https://aclanthology.org/2022.acl-long.192.pdf | acl-2022-5 | ['few-shot-text-classification'] | ['natural-language-processing'] | [-1.88267939e-02 -4.90594395e-02 -3.88014689e-02 -6.18988514e-01
-1.05154133e+00 -6.75222635e-01 5.16389489e-01 2.53457427e-01
-1.01835573e+00 9.32328880e-01 3.70798320e-01 -4.05516177e-01
2.70258579e-02 -4.77055788e-01 -3.27465326e-01 -2.43097454e-01
9.62578654e-02 3.35755259e-01 4.16713119e-01 -1.73452348... | [10.5513916015625, 8.698673248291016] |
441283a4-5949-435e-af1f-90e888ee5859 | on-board-change-detection-for-resource | 2305.10119 | null | https://arxiv.org/abs/2305.10119v1 | https://arxiv.org/pdf/2305.10119v1.pdf | On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites | The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate. This paper considers problem of efficient downlink communication of multi-spectral satellite images for Earth observation using change detection. The prop... | ['Petar Popovski', 'Eva Lagunas', 'Shashi Raj Pandey', 'Israel Leyva-Mayorga', 'Thinh Q. Dinh', 'Van-Phuc Bui'] | 2023-05-17 | null | null | null | null | ['cloud-removal', 'change-detection'] | ['computer-vision', 'computer-vision'] | [ 5.87985039e-01 -4.18012500e-01 -5.42259729e-03 -2.55803168e-01
-3.24836314e-01 -5.12073815e-01 3.30171824e-01 2.06472680e-01
-4.16904002e-01 5.18092871e-01 1.62239417e-01 -2.87221551e-01
-4.42487180e-01 -1.13167810e+00 -3.51346046e-01 -1.12015212e+00
-4.62821394e-01 2.90366337e-02 1.70800999e-01 -1.70369089... | [10.237542152404785, -1.9072110652923584] |
23eaf275-375e-4985-908a-f7e76ffc6cbf | opengait-revisiting-gait-recognition-toward | 2211.06597 | null | https://arxiv.org/abs/2211.06597v3 | https://arxiv.org/pdf/2211.06597v3.pdf | OpenGait: Revisiting Gait Recognition Toward Better Practicality | Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly,... | ['Shiqi Yu', 'Yongzhen Huang', 'Saihui Hou', 'Chuanfu Shen', 'Junhao Liang', 'Chao Fan'] | 2022-11-12 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-1.17321700e-01 -6.81662202e-01 -3.48819315e-01 -1.89679921e-01
-5.63528478e-01 -4.95761842e-01 3.03418070e-01 -2.81893194e-01
-2.58871228e-01 8.37594330e-01 2.77229875e-01 -1.69184521e-01
-2.53589869e-01 -6.62542224e-01 -3.66014898e-01 -8.74200881e-01
-3.43641788e-01 4.58850153e-02 2.89828569e-01 -2.03810051... | [14.286940574645996, 1.4169251918792725] |
473af198-c3b9-42ae-bb72-a4e92af6110a | shi-he-jian-dong-ren-shi-yong-zhi-yu-yin-1 | null | null | https://aclanthology.org/2019.rocling-1.16 | https://aclanthology.org/2019.rocling-1.16.pdf | 適合漸凍人使用之語音轉換系統初步研究(Deep Neural-Network Bandwidth Extension and Denoising Voice Conversion System for ALS Patients) | null | ['Daniel Hládek', 'Matúš Pleva', 'Yuan-Fu Liao', 'Bai-Hong Huang'] | null | null | null | null | rocling-2019-10 | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.159940719604492, 3.809748649597168] |
d23ab62d-eea2-487d-bc95-a2d59b959d0e | functional-object-oriented-network-1 | 1905.00502 | null | https://arxiv.org/abs/1905.00502v4 | https://arxiv.org/pdf/1905.00502v4.pdf | Task Planning with a Weighted Functional Object-Oriented Network | In reality, there is still much to be done for robots to be able to perform manipulation actions with full autonomy. Complicated manipulation tasks, such as cooking, may still require a person to perform some actions that are very risky for a robot to perform. On the other hand, some other actions may be very risky for... | ['Yu Sun', 'David Paulius', 'Kelvin Sheng Pei Dong'] | 2019-05-01 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [ 2.13779986e-01 7.50887215e-01 2.49583393e-01 -4.77500260e-01
2.99305022e-01 2.72682286e-04 2.92355008e-03 -6.50725663e-02
-7.03046799e-01 8.55951786e-01 -1.93323329e-01 9.90025997e-02
-6.13924086e-01 -7.31025279e-01 -3.37493449e-01 -5.63335121e-01
-2.08198905e-01 6.15477800e-01 4.57715601e-01 -4.37180012... | [4.876224517822266, 0.9755404591560364] |
5382bc92-6d2c-4fe9-8c1a-e6288f5a3f3c | fooling-neural-network-interpretations-via | 1902.02041 | null | https://arxiv.org/abs/1902.02041v3 | https://arxiv.org/pdf/1902.02041v3.pdf | Fooling Neural Network Interpretations via Adversarial Model Manipulation | We ask whether the neural network interpretation methods can be fooled via adversarial model manipulation, which is defined as a model fine-tuning step that aims to radically alter the explanations without hurting the accuracy of the original models, e.g., VGG19, ResNet50, and DenseNet121. By incorporating the interpre... | ['Juyeon Heo', 'Taesup Moon', 'Sunghwan Joo'] | 2019-02-06 | fooling-neural-network-interpretations-via-1 | http://papers.nips.cc/paper/8558-fooling-neural-network-interpretations-via-adversarial-model-manipulation | http://papers.nips.cc/paper/8558-fooling-neural-network-interpretations-via-adversarial-model-manipulation.pdf | neurips-2019-12 | ['network-interpretation'] | ['computer-vision'] | [ 4.65297431e-01 9.70655084e-01 2.16846973e-01 -3.18409592e-01
-2.79425263e-01 -8.44804168e-01 8.50627422e-01 -3.59197348e-01
-3.07523519e-01 8.81110191e-01 -1.14995539e-01 -2.96129704e-01
-9.71111357e-02 -5.51843822e-01 -1.18431067e+00 -5.47082424e-01
2.78252810e-01 4.42158759e-01 4.32799071e-01 -4.40963447... | [5.838761329650879, 7.752811908721924] |
de7b23e9-ed5b-4469-827f-92f84ac89c5b | hegel-a-novel-dataset-for-geo-location-from | 2307.00509 | null | https://arxiv.org/abs/2307.00509v1 | https://arxiv.org/pdf/2307.00509v1.pdf | HeGeL: A Novel Dataset for Geo-Location from Hebrew Text | The task of textual geolocation - retrieving the coordinates of a place based on a free-form language description - calls for not only grounding but also natural language understanding and geospatial reasoning. Even though there are quite a few datasets in English used for geolocation, they are currently based on open-... | ['Reut Tsarfaty', 'Sagi Dalyot', 'Itzhak Omer', 'Itai Mondshine', 'Tal Bauman', 'Tzuf Paz-Argaman'] | 2023-07-02 | null | null | null | null | ['retrieval', 'natural-language-understanding'] | ['methodology', 'natural-language-processing'] | [-5.81489503e-01 1.76840078e-03 -4.30660546e-01 -3.80428582e-01
-9.00734425e-01 -8.95189881e-01 1.03758526e+00 7.60676920e-01
-8.62310708e-01 9.53097641e-01 1.11981618e+00 -4.38796103e-01
-2.13993728e-01 -1.24212921e+00 -5.32548726e-01 -3.56530219e-01
1.89479843e-01 6.31152749e-01 1.06851600e-01 -5.80405712... | [9.413247108459473, 9.126927375793457] |
2e785919-99aa-4988-9754-a6d948f0afdc | contact-graspnet-efficient-6-dof-grasp | 2103.14127 | null | https://arxiv.org/abs/2103.14127v1 | https://arxiv.org/pdf/2103.14127v1.pdf | Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes | Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, existing approaches often consist of complex sequential pipelines that possess several potential failure points and run-times unsuitable for c... | ['Dieter Fox', 'Rudolph Triebel', 'Arsalan Mousavian', 'Martin Sundermeyer'] | 2021-03-25 | null | null | null | null | ['grasp-generation'] | ['computer-vision'] | [-2.67038252e-02 -7.66360909e-02 2.25977093e-01 -2.86121905e-01
-7.98239052e-01 -1.00435293e+00 1.67765826e-01 8.77446458e-02
-2.12688789e-01 2.18578592e-01 -1.58741996e-01 -1.38111010e-01
-3.76246750e-01 -5.55927813e-01 -1.26155448e+00 -5.21183550e-01
-5.62414944e-01 1.16719842e+00 3.58276874e-01 -1.04718491... | [5.708368301391602, -0.7903645634651184] |
ca5793c0-271d-497b-aa21-da4e7ff0a3d9 | large-sequence-models-for-sequential-decision | 2306.13945 | null | https://arxiv.org/abs/2306.13945v1 | https://arxiv.org/pdf/2306.13945v1.pdf | Large Sequence Models for Sequential Decision-Making: A Survey | Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e.g., GPT-3 and Swin Transformer. Although originally designed for prediction problems, it is natural to inquire about their suitability ... | ['Weinan Zhang', 'Haifeng Zhang', 'Jun Wang', 'Luo Mai', 'Ying Wen', 'Yaodong Yang', 'Hanjing Wang', 'Runji Lin', 'Muning Wen'] | 2023-06-24 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 3.45870465e-01 4.30317596e-02 -5.53548634e-01 -2.68797994e-01
-3.91234815e-01 -3.26263815e-01 3.78917038e-01 1.68740675e-01
-4.52877820e-01 6.41010642e-01 -2.14338139e-01 -6.62433088e-01
-1.45241916e-01 -7.55663157e-01 -2.90912539e-01 -7.65837967e-01
-1.29468426e-01 6.58425570e-01 9.29068625e-02 -2.12493256... | [10.845123291015625, 6.629647731781006] |
a26ac538-e362-4385-863e-538cd64f529e | coarse-to-fine-seam-estimation-for-image | 1805.09578 | null | http://arxiv.org/abs/1805.09578v1 | http://arxiv.org/pdf/1805.09578v1.pdf | Coarse-to-fine Seam Estimation for Image Stitching | Seam-cutting and seam-driven techniques have been proven effective for
handling imperfect image series in image stitching. Generally, seam-driven is
to utilize seam-cutting to find a best seam from one or finite alignment
hypotheses based on a predefined seam quality metric. However, the quality
metrics in most methods... | ['Yifang Xu', 'Tianli Liao', 'Jing Chen'] | 2018-05-24 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 5.19569576e-01 -3.91844332e-01 1.99904040e-01 -2.67409563e-01
-6.99013829e-01 -3.05933923e-01 2.82002181e-01 6.99853105e-03
-2.25081086e-01 4.01320994e-01 6.09741770e-02 1.67145655e-01
-3.23158592e-01 -6.85730219e-01 -5.83175898e-01 -8.31438839e-01
2.73304909e-01 8.75668749e-02 7.42952824e-01 -4.18525696... | [9.489852905273438, -2.2210853099823] |
2a9f0b67-2a92-42cb-a8c5-85be8947eeae | the-monitor-model-and-its-misconceptions-a | 2210.14367 | null | https://arxiv.org/abs/2210.14367v2 | https://arxiv.org/pdf/2210.14367v2.pdf | The Monitor Model and its Misconceptions: A Clarification | Horizontal (automatic) and vertical (control) processes have been observed and reported for a long time in translation production. Schaeffer and Carl's Monitor Model integrates these two processes into one framework, assuming that priming mechanisms underlie horizontal/automatic processes, while vertical/monitoring pro... | ['Michael Carl'] | 2022-10-25 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 1.78090125e-01 3.02475899e-01 -5.84515572e-01 -1.78562433e-01
-1.11342020e-01 -9.74194467e-01 1.33203030e+00 6.75933838e-01
-2.26986229e-01 2.68009037e-01 7.12846637e-01 -9.92304921e-01
-1.16120480e-01 -3.07928830e-01 -7.38361031e-02 -2.53598422e-01
5.19669890e-01 3.25372964e-01 7.06480145e-02 -2.62780339... | [10.167875289916992, 8.515925407409668] |
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